Zulkifli Yusof - Academia.edu (original) (raw)

Papers by Zulkifli Yusof

Research paper thumbnail of Rule-based multi-state gravitational search algorithm for discrete optimization problem

2015 4th International Conference on Software Engineering and Computer Systems (ICSECS), 2015

Research paper thumbnail of A Multi-state Gravitational Search Algorithm for Combinatorial Optimization Problems

2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, 2015

Research paper thumbnail of An Image Registration Technique To Enhance PCB Inspection Algorithm with Real Images

Research paper thumbnail of A Particle Swarm Optimization Approach to Robotic Drill Route Optimization

2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, 2010

Research paper thumbnail of A Binary Particle Swarm Optimization Approach to Optimize Assembly Sequence Planning

Advanced Science Letters, 2012

Research paper thumbnail of Iterative RLC Models for Interconnect Delay Optimization in Vlsi Routing Algorithms

Research paper thumbnail of Non-parametric induction motor rotor flux estimator based on feed-forward neural network

International Journal of Power Electronics and Drive Systems (IJPEDS)

The conventional induction motor rotor flux observer based on current model and voltage model are... more The conventional induction motor rotor flux observer based on current model and voltage model are sensitive to parameter uncertainties. In this paper, a non-parametric induction motor rotor flux estimator based on feed-forward neural network is proposed. This estimator is operating without motor parameters and therefore it is independent from parameter uncertainties. The model is trained using Levenberg-Marquardt algorithm offline. All the data collection, training and testing process are fully performed in MATLAB/Simulink environment. A forced iteration of 1,000-epochs is imposed in the training process. There are overall 603,968 datasets are used in this modeling process. This four-input two-output neural network model is capable of providing rotor flux estimation for field-oriented control systems with 3.41e-9 mse and elapsed 28 minutes 49 seconds training time consumption. This proposed model is tested with reference speed step response and parameters uncertainties. The result i...

Research paper thumbnail of Feature Selection Using Binary Simulated Kalman Filter for Peak Classification of EEG Signals

2018 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2018

Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for... more Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this paper, another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection problem. It is found that the BSKF algorithm performed slightly better than the AMSKF algorithm.

Research paper thumbnail of A Novel Multi-State Gravitational Search Algorithm for Discrete Optimization Problems

International Journal of Simulation Systems Science & Technology, 2020

The binary-based algorithms including the binary gravitational search algorithm (BGSA) were desig... more The binary-based algorithms including the binary gravitational search algorithm (BGSA) were designed to solve discrete optimization problems. Many improvements of the binary-based algorithms have been reported. In this paper, a variant of GSA called multi-state gravitational search algorithm (MSGSA) for discrete optimization problems is proposed. The MSGSA concept is based on a simplified mechanism of transition between two states. The performance of the MSGSA is empirically compared to the original BGSA based on six sets of selected benchmarks instances of traveling salesman problem (TSP). The results are statistically analyzed and show that the MSGSA has performed consistently in solving the discrete optimization problems.

Research paper thumbnail of Multi-State Particle Swarm Optimization for Discrete Combinatorial Optimization Problem

International Journal of Simulation Systems Science & Technology, 2020

The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are... more The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve discrete optimization problems. Many works have focused on the improvement of the binary-based algorithms. Yet, none of these works have been represented in states. In this paper, by implementing the representation of state in particle swarm optimization (PSO), a variant of PSO called multi-state particle swarm optimization (MSPSO) algorithm is proposed. The proposed algorithm works based on a simplified mechanism of transition between two states. The performance of MSPSO algorithm is emperically compared to BPSO and other two binary-based algorithms on six sets of selected benchmarks instances of traveling salesman problem (TSP). The experimental results showed that the newly introduced approach manage to obtain comparable results, compared to other algorithms in consideration.

Research paper thumbnail of A Modified Gravitational Search Algorithm for Discrete Optimization Problem

International Journal of Simulation Systems Science & Technology, 2020

This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational... more This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.

Research paper thumbnail of Improving Black Hole Algorithm using Gravitational Search, White Hole Operator, and Local Search

Mekatronika, 2019

Previously, the black hole (BH) algorithm has been subjected to various fundamental enhancements.... more Previously, the black hole (BH) algorithm has been subjected to various fundamental enhancements. Among others, white hole operator and local search have been embedded in the BH algorithm to improve its performance significantly. This paper shows that combination of gravitational search, white hole operator, and local search also able to improve the performance of the BH algorithm significantly.

Research paper thumbnail of A Tutorial on Single-solution Simulated Kalman Filter

MEKATRONIKA, 2019

Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established ... more Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established based on the Kalman filtering framework. A variant of SKF which operates using one agent is called single-solution simulated Kalman filter (ssSKF). At present, there is no tutorial been published on ssSKF. One may find that the equations and flowchart of the algorithm is not easy to understand. Hence, this paper provides a tutorial on ssSKF algorithm that emphasizes on a numerical example for easy and intuitive explanations. This tutorial would be important to those who work on the fundamentals and applications of ssSKF as well as to students who are new to optimization research.

Research paper thumbnail of BSKF: Simulated Kalman Filter

2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), 2015

Inspired by the estimation capab filter, we have recently introduced a novel optimization algorit... more Inspired by the estimation capab filter, we have recently introduced a novel optimization algorithm called simulated Kal Every agent in SKF is regarded as a Kalman the mechanism of Kalman filtering and meas every agent estimates the global min Measurement, which is required in Kalm mathematically modelled and simulated. Age among them to update and improve the sol search process. However, the SKF is only continuous numerical optimization problem. combinatorial optimization problems, an ext SKF algorithm, which is termed as Binary proposed. Similar to existing approach, a ma used to enable the SKF algorithm to operate space. A set of traveling salesman proble evaluate the performance of the propose Binary Gravitational Search Algorithm (BG Particle Swarm Optimization (BPSO).

Research paper thumbnail of Binary Particle Swarm Optimization Approach for Random Generation Outage Maintenance Scheduling

ICTACT Journal on Soft Computing, 2013

This paper presents a methodology for maintenance scheduling (MS) of generators using binary part... more This paper presents a methodology for maintenance scheduling (MS) of generators using binary particle swarm optimization (BPSO) based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP) for a power system. The capacity outage probability table (COPT) is the initial step in creating maintenance schedule using the probabilistic levelized risk method. This paper proposes BPSO method which is used to construct the COPT. In order to mitigate the effects of probabilistic levelized risk method, BPSO based probabilistic levelized risk method is embarked on a MS problem. In order to validate the effectiveness of the proposed algorithm, case study results for simple five unit system can accomplish a significant levelization in the reliability indices that make possible to evaluate system generation system adequacy in the MS horizon of the power system. The proposed method shows better performance compared with other optimization methods and conventional method with improved search performance.

Research paper thumbnail of An Assembly Sequence Planning Approach with a Multi-state Particle Swarm Optimization

Lecture Notes in Computer Science, 2016

Assembly sequence planning (ASP) becomes one of the major challenges in the product design and ma... more Assembly sequence planning (ASP) becomes one of the major challenges in the product design and manufacturing. A good assembly sequence leads in reducing the cost and time of the manufacturing process. However, assembly sequence planning is known as a classical hard combinatorial optimization problem. Assembly sequence planning with more product components becomes more difficult to be solved. In this paper, an approach based on a new variant of Particle Swarm Optimization Algorithm (PSO) called the multi-state of Particle Swarm Optimization (MSPSO) is used to solve the assembly sequence planning problem. As in of Particle Swarm Optimization Algorithm, MSPSO incorporates the swarming behaviour of animals and human social behaviour, the best previous experience of each individual member of swarm, the best previous experience of all other members of swarm, and a rule which makes each assembly component of each individual solution of each individual member is occurred once based on precedence constraints and the best feasible sequence of assembly is then can be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and comparison has been conducted against other three approaches based on Simulated Annealing (SA), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement.

Research paper thumbnail of A Hybrid of Harmony Search and Minimization of Metabolic Adjustment for Optimization of Succinic Acid Production

Advances in Intelligent Systems and Computing, 2016

Succinic acid has been favored by researchers due to its industrial multi-uses. However, the prod... more Succinic acid has been favored by researchers due to its industrial multi-uses. However, the production of succinic acid is far below cell theoretical maximum. The goal of this research is to identify the optimal set of gene knockouts for obtaining high production of succinic acid in microorganisms. Gene knockout is a widely used genetic engineering technique. Hence, a hybrid of Harmony Search (HS) and Minimization of Metabolic Adjustment (MOMA) is proposed. The dataset applied is a core Escherichia coli metabolic network model. Harmony Search is a meta-heuristic algorithm inspired by musicians' improvisation process. Minimization of Metabolic Adjustment is used to calculate fitness closest to the wild-type, after mutant gene knockout. The result obtained from the proposed hybrid technique are knockout genes list and production rate after the deletion. This proposed technique is possible to be applied in wet laboratory experiment to increase the production of succinic acid in E. coli.

Research paper thumbnail of Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

Advances in Intelligent Systems and Computing, 2016

Genetic engineering provides methods to modify the genes of microorganisms to achieve desired eff... more Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. This can be done for improved organism growth rate or increasing production yield of a desired gene product. Gene knockout is a technique that can improve the specific characteristics of microorganisms by disabling selected sets of genes. However, microorganisms are complex and predicting the effects of gene modification is difficult. Several algorithms have been proposed to support a range of gene knockout strategies, including BAFBA, BHFBA and DBFBA. In this paper, scaling these algorithms and methods to utilise High Performance Computing (HPC) resources have been explored. The applications have been parallelized on HPC and the scalability and performance of these approaches were explored and documented.

Research paper thumbnail of An Improved Multi-state Particle Swarm Optimization for Discrete Optimization Problems

2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, 2015

Particle swarm optimization (PSO) has been successfully applied to solve various optimization pro... more Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. However, the MSPSO algorithm has to deal with the production of infeasible solutions and hence, additional step to convert the infeasible solution to feasible solution is required. In this paper, the MSPSO is improved by introducing a strategy that directly produces feasible solutions. The performance of the improved multi-state particle swarm optimization (IMSPSO) is empirically evaluated based on a set of travelling salesman problems (TSPs). The experimental results showed the newly introduced approach is promising and consistently outperformed the binary PSO algorithm.

Research paper thumbnail of A Novel Multi-state Particle Swarm Optimization for Discrete Combinatorial Optimization Problems

2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, 2012

Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems... more Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state particle swarm optimization (MSPSO) is proposed to solve discrete optimization problems. Instead of evolving a high dimensional bit vector as in BinPSO, the proposed MSPSO mechanism evolves states of variables involved. The MSPSO algorithm has been applied to two benchmark instances of traveling salesman problem (TSP). The experimental results show that the the proposed MSPSO algorithm consistently outperforms the BinPSO in solving the discrete combinatorial optimization problem.

Research paper thumbnail of Rule-based multi-state gravitational search algorithm for discrete optimization problem

2015 4th International Conference on Software Engineering and Computer Systems (ICSECS), 2015

Research paper thumbnail of A Multi-state Gravitational Search Algorithm for Combinatorial Optimization Problems

2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, 2015

Research paper thumbnail of An Image Registration Technique To Enhance PCB Inspection Algorithm with Real Images

Research paper thumbnail of A Particle Swarm Optimization Approach to Robotic Drill Route Optimization

2010 Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, 2010

Research paper thumbnail of A Binary Particle Swarm Optimization Approach to Optimize Assembly Sequence Planning

Advanced Science Letters, 2012

Research paper thumbnail of Iterative RLC Models for Interconnect Delay Optimization in Vlsi Routing Algorithms

Research paper thumbnail of Non-parametric induction motor rotor flux estimator based on feed-forward neural network

International Journal of Power Electronics and Drive Systems (IJPEDS)

The conventional induction motor rotor flux observer based on current model and voltage model are... more The conventional induction motor rotor flux observer based on current model and voltage model are sensitive to parameter uncertainties. In this paper, a non-parametric induction motor rotor flux estimator based on feed-forward neural network is proposed. This estimator is operating without motor parameters and therefore it is independent from parameter uncertainties. The model is trained using Levenberg-Marquardt algorithm offline. All the data collection, training and testing process are fully performed in MATLAB/Simulink environment. A forced iteration of 1,000-epochs is imposed in the training process. There are overall 603,968 datasets are used in this modeling process. This four-input two-output neural network model is capable of providing rotor flux estimation for field-oriented control systems with 3.41e-9 mse and elapsed 28 minutes 49 seconds training time consumption. This proposed model is tested with reference speed step response and parameters uncertainties. The result i...

Research paper thumbnail of Feature Selection Using Binary Simulated Kalman Filter for Peak Classification of EEG Signals

2018 8th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 2018

Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for... more Previously, an angle modulated simulated Kalman filter (AMSKF) algorithm has been implemented for feature selection in peak classification of electroencephalogram (EEG) signals. The AMSKF is an extension of simulated Kalman filter (SKF) algorithm for combinatorial optimization problems. In this paper, another extension of SKF algorithm, which is called binary SKF (BSKF) algorithm, is applied for the same feature selection problem. It is found that the BSKF algorithm performed slightly better than the AMSKF algorithm.

Research paper thumbnail of A Novel Multi-State Gravitational Search Algorithm for Discrete Optimization Problems

International Journal of Simulation Systems Science & Technology, 2020

The binary-based algorithms including the binary gravitational search algorithm (BGSA) were desig... more The binary-based algorithms including the binary gravitational search algorithm (BGSA) were designed to solve discrete optimization problems. Many improvements of the binary-based algorithms have been reported. In this paper, a variant of GSA called multi-state gravitational search algorithm (MSGSA) for discrete optimization problems is proposed. The MSGSA concept is based on a simplified mechanism of transition between two states. The performance of the MSGSA is empirically compared to the original BGSA based on six sets of selected benchmarks instances of traveling salesman problem (TSP). The results are statistically analyzed and show that the MSGSA has performed consistently in solving the discrete optimization problems.

Research paper thumbnail of Multi-State Particle Swarm Optimization for Discrete Combinatorial Optimization Problem

International Journal of Simulation Systems Science & Technology, 2020

The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are... more The binary-based algorithms including the binary particle swarm optimization (BPSO) algorithm are proposed to solve discrete optimization problems. Many works have focused on the improvement of the binary-based algorithms. Yet, none of these works have been represented in states. In this paper, by implementing the representation of state in particle swarm optimization (PSO), a variant of PSO called multi-state particle swarm optimization (MSPSO) algorithm is proposed. The proposed algorithm works based on a simplified mechanism of transition between two states. The performance of MSPSO algorithm is emperically compared to BPSO and other two binary-based algorithms on six sets of selected benchmarks instances of traveling salesman problem (TSP). The experimental results showed that the newly introduced approach manage to obtain comparable results, compared to other algorithms in consideration.

Research paper thumbnail of A Modified Gravitational Search Algorithm for Discrete Optimization Problem

International Journal of Simulation Systems Science & Technology, 2020

This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational... more This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.

Research paper thumbnail of Improving Black Hole Algorithm using Gravitational Search, White Hole Operator, and Local Search

Mekatronika, 2019

Previously, the black hole (BH) algorithm has been subjected to various fundamental enhancements.... more Previously, the black hole (BH) algorithm has been subjected to various fundamental enhancements. Among others, white hole operator and local search have been embedded in the BH algorithm to improve its performance significantly. This paper shows that combination of gravitational search, white hole operator, and local search also able to improve the performance of the BH algorithm significantly.

Research paper thumbnail of A Tutorial on Single-solution Simulated Kalman Filter

MEKATRONIKA, 2019

Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established ... more Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established based on the Kalman filtering framework. A variant of SKF which operates using one agent is called single-solution simulated Kalman filter (ssSKF). At present, there is no tutorial been published on ssSKF. One may find that the equations and flowchart of the algorithm is not easy to understand. Hence, this paper provides a tutorial on ssSKF algorithm that emphasizes on a numerical example for easy and intuitive explanations. This tutorial would be important to those who work on the fundamentals and applications of ssSKF as well as to students who are new to optimization research.

Research paper thumbnail of BSKF: Simulated Kalman Filter

2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), 2015

Inspired by the estimation capab filter, we have recently introduced a novel optimization algorit... more Inspired by the estimation capab filter, we have recently introduced a novel optimization algorithm called simulated Kal Every agent in SKF is regarded as a Kalman the mechanism of Kalman filtering and meas every agent estimates the global min Measurement, which is required in Kalm mathematically modelled and simulated. Age among them to update and improve the sol search process. However, the SKF is only continuous numerical optimization problem. combinatorial optimization problems, an ext SKF algorithm, which is termed as Binary proposed. Similar to existing approach, a ma used to enable the SKF algorithm to operate space. A set of traveling salesman proble evaluate the performance of the propose Binary Gravitational Search Algorithm (BG Particle Swarm Optimization (BPSO).

Research paper thumbnail of Binary Particle Swarm Optimization Approach for Random Generation Outage Maintenance Scheduling

ICTACT Journal on Soft Computing, 2013

This paper presents a methodology for maintenance scheduling (MS) of generators using binary part... more This paper presents a methodology for maintenance scheduling (MS) of generators using binary particle swarm optimization (BPSO) based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP) for a power system. The capacity outage probability table (COPT) is the initial step in creating maintenance schedule using the probabilistic levelized risk method. This paper proposes BPSO method which is used to construct the COPT. In order to mitigate the effects of probabilistic levelized risk method, BPSO based probabilistic levelized risk method is embarked on a MS problem. In order to validate the effectiveness of the proposed algorithm, case study results for simple five unit system can accomplish a significant levelization in the reliability indices that make possible to evaluate system generation system adequacy in the MS horizon of the power system. The proposed method shows better performance compared with other optimization methods and conventional method with improved search performance.

Research paper thumbnail of An Assembly Sequence Planning Approach with a Multi-state Particle Swarm Optimization

Lecture Notes in Computer Science, 2016

Assembly sequence planning (ASP) becomes one of the major challenges in the product design and ma... more Assembly sequence planning (ASP) becomes one of the major challenges in the product design and manufacturing. A good assembly sequence leads in reducing the cost and time of the manufacturing process. However, assembly sequence planning is known as a classical hard combinatorial optimization problem. Assembly sequence planning with more product components becomes more difficult to be solved. In this paper, an approach based on a new variant of Particle Swarm Optimization Algorithm (PSO) called the multi-state of Particle Swarm Optimization (MSPSO) is used to solve the assembly sequence planning problem. As in of Particle Swarm Optimization Algorithm, MSPSO incorporates the swarming behaviour of animals and human social behaviour, the best previous experience of each individual member of swarm, the best previous experience of all other members of swarm, and a rule which makes each assembly component of each individual solution of each individual member is occurred once based on precedence constraints and the best feasible sequence of assembly is then can be determined. To verify the feasibility and performance of the proposed approach, a case study has been performed and comparison has been conducted against other three approaches based on Simulated Annealing (SA), Genetic Algorithm (GA), and Binary Particle Swarm Optimization (BPSO). The experimental results show that the proposed approach has achieved significant improvement.

Research paper thumbnail of A Hybrid of Harmony Search and Minimization of Metabolic Adjustment for Optimization of Succinic Acid Production

Advances in Intelligent Systems and Computing, 2016

Succinic acid has been favored by researchers due to its industrial multi-uses. However, the prod... more Succinic acid has been favored by researchers due to its industrial multi-uses. However, the production of succinic acid is far below cell theoretical maximum. The goal of this research is to identify the optimal set of gene knockouts for obtaining high production of succinic acid in microorganisms. Gene knockout is a widely used genetic engineering technique. Hence, a hybrid of Harmony Search (HS) and Minimization of Metabolic Adjustment (MOMA) is proposed. The dataset applied is a core Escherichia coli metabolic network model. Harmony Search is a meta-heuristic algorithm inspired by musicians' improvisation process. Minimization of Metabolic Adjustment is used to calculate fitness closest to the wild-type, after mutant gene knockout. The result obtained from the proposed hybrid technique are knockout genes list and production rate after the deletion. This proposed technique is possible to be applied in wet laboratory experiment to increase the production of succinic acid in E. coli.

Research paper thumbnail of Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

Advances in Intelligent Systems and Computing, 2016

Genetic engineering provides methods to modify the genes of microorganisms to achieve desired eff... more Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. This can be done for improved organism growth rate or increasing production yield of a desired gene product. Gene knockout is a technique that can improve the specific characteristics of microorganisms by disabling selected sets of genes. However, microorganisms are complex and predicting the effects of gene modification is difficult. Several algorithms have been proposed to support a range of gene knockout strategies, including BAFBA, BHFBA and DBFBA. In this paper, scaling these algorithms and methods to utilise High Performance Computing (HPC) resources have been explored. The applications have been parallelized on HPC and the scalability and performance of these approaches were explored and documented.

Research paper thumbnail of An Improved Multi-state Particle Swarm Optimization for Discrete Optimization Problems

2015 7th International Conference on Computational Intelligence, Communication Systems and Networks, 2015

Particle swarm optimization (PSO) has been successfully applied to solve various optimization pro... more Particle swarm optimization (PSO) has been successfully applied to solve various optimization problems. Recently, a state-based algorithm called multi-state particle swarm optimization (MSPSO) has been proposed to solve discrete combinatorial optimization problems. The algorithm operates based on a simplified mechanism of transition between two states. However, the MSPSO algorithm has to deal with the production of infeasible solutions and hence, additional step to convert the infeasible solution to feasible solution is required. In this paper, the MSPSO is improved by introducing a strategy that directly produces feasible solutions. The performance of the improved multi-state particle swarm optimization (IMSPSO) is empirically evaluated based on a set of travelling salesman problems (TSPs). The experimental results showed the newly introduced approach is promising and consistently outperformed the binary PSO algorithm.

Research paper thumbnail of A Novel Multi-state Particle Swarm Optimization for Discrete Combinatorial Optimization Problems

2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation, 2012

Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems... more Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state particle swarm optimization (MSPSO) is proposed to solve discrete optimization problems. Instead of evolving a high dimensional bit vector as in BinPSO, the proposed MSPSO mechanism evolves states of variables involved. The MSPSO algorithm has been applied to two benchmark instances of traveling salesman problem (TSP). The experimental results show that the the proposed MSPSO algorithm consistently outperforms the BinPSO in solving the discrete combinatorial optimization problem.