A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO (original) (raw)

A time-varying mirrored S-shaped transfer function for binary particle swarm optimization

Information Sciences, 2020

Binary Particle swarm optimization (BPSO) is one of the most popular swarm intelligence algorithms to solve binary optimization problems. It has a few parameters, simple structure, and high execution speed. A transfer function is applied in BPSO to convert the continuous search space to the binary one. This algorithm and its variants can sometimes find local optima or exhibit slow convergence speed. Thus, many researchers have improved the structure of BPSO and its transfer function to overcome these shortcomings. In this study, a new time-varying mirrored S-shaped transfer function for BPSO (TVMS-BPSO) is introduced to enhance global exploration and local exploitation in the algorithm. The performance of the proposed transfer function has been compared with some well-known BPSO algorithms and binary meta-heuristic algorithms. These algorithms have been evaluated by CEC 2005 benchmark functions and set of 0–1 multidimensional knapsack problem (MKP) benchmark instances. The experimental results showed that the new transfer function significantly enhances the efficiency of BPSO for both local and global topologies in terms of solution accuracy and convergence speed.

A novel x-shaped binary particle swarm optimization

Soft Computing, 2021

Definitive optimization algorithms are not able to solve high-dimensional optimization problems because the search space grows exponentially with the problem size and an exhaustive search will be impractical. Therefore, approximate algorithms are applied to solve them. A category of approximate algorithms are meta-heuristic algorithms. They have shown an acceptable efficiency to solve these problems. Among them, particle swarm optimization (PSO) is one of the well-known swarm intelligence algorithms to optimize continuous problems. A transfer function is applied in this algorithm to convert the continuous search space to the binary one. The role of the transfer function in binary PSO (BPSO) is very important to enhance its performance. Several transfer functions have been proposed for BPSO such as S-shaped, V-shaped, linear and other transfer functions. However, BPSO algorithm can sometimes find local optima or show slow convergence speed in some problems because of using the velocity of PSO and these transfer functions. In this study, a novel transfer function called x-shaped BPSO (XBPSO) is proposed to increase exploration and exploitation of BPSO in the binary search space. The transfer function uses two functions and improved rules to generate a new binary solution. The proposed method has been run on 33 benchmark instances of the 0–1 multidimensional knapsack problem (MKP), two discrete maximization functions and 23 minimization functions. The results have been compared with some well-known BPSO and discrete meta-heuristic algorithms. The results showed that x-shaped transfer function considerably increased the solution accuracy and convergence speed in BPSO algorithm. The average error of compared algorithms on all 0–1 MKP benchmark instances indicated that XBPSO has the minimum error of 8.9%. Also, the mean absolute error (MAE) obtained by XBPSO on two discrete maximization functions is 0.45. Moreover, the proposed transfer function provides superior solutions in 18 functions from 23 minimization functions.

Binary particle swarm optimization: challenges and solutions

Particle Swarm Optimization (PSO) algorithm, originated as a simulation of a simplified social system, is an evolutionary computation technique developed successfully in recent years and have been applied to many optimization problems. PSO can be applied to continuous and discrete optimization problems through local and global models. In this paper, PSO is addressed in details. There are some difficulties with the standard PSO where causing slow convergence rate on some optimization problems. These difficulties are transferred to the origin binary PSO (BPSO) that makes the algorithm not to converge well. Due to these difficulties with the BPSO, in this paper a new BPSO (NBPSO) is introduced. Several benchmark problems including unimodal and multimodal functions are considered for testing the robustness and effectiveness of the proposed method over the original BPSO. The results show that NBPSO performs much better than BPSO. Since the obtained results show that NBPSO may trap in the local optima, further modification is carried out. Two different methods are suggested to improve NBPSO which are denoted as Guaranteed Convergence BPSO (GCBPSO) and Improved NBPSO (INBPSO). The results show the superiority of the INBPSO for solving optimization problems.

Memetic binary particle swarm optimization for discrete optimization problems

Information Sciences, 2015

In recent decades, many researchers have been interested in algorithms inspired by the observation of natural phenomena to solve optimization problems. Among them, meta-heuristic algorithms have been extensively applied in continuous (real) and discrete (binary) search spaces. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. In this study, a memetic binary particle swarm optimization (BPSO) scheme is introduced based on hybrid local and global searches in BPSO. The algorithm, binary hybrid topology particle swarm optimization (BHTPSO), is used to solve the optimization problems in the binary search spaces. In addition, a variant of the proposed algorithm, binary hybrid topology particle swarm optimization quadratic interpolation (BHTPSO-QI), is proposed to enhance the global searching capability. These algorithms are tested on two set of problems in the binary search space. Several nonlinear high-dimension functions and benchmarks for the 0-1 multidimensional knapsack problem (MKP) are employed to evaluate their performances. Their results are compared with some well-known modified binary PSO and binary gravitational search algorithm (BGSA). The experimental results showed that the proposed methods improve the performance of BPSO in terms of convergence speed and solution accuracy.

Novel Multi-swarm Approach for Balancing Exploration and Exploitation in Particle Swarm Optimization

Advances in Intelligent Systems and Computing

Several metaheuristic algorithms and improvements to the existing ones have been presented over the years. Most of these algorithms were inspired either by nature or the behavior of certain swarms, such as birds, ants, bees, or even bats. These algorithms have two major components, which are exploration and exploitation. The interaction of these components can have a significant influence on the efficiency of the metaheuristics. Meanwhile, there are basically no guiding principles on how to strike a balance between these two components. This study, therefore, proposes a new multi-swarm-based balancing mechanism for keeping a balancing between the exploration and exploitation attributes of metaheuristics. The new approach is inspired by the phenomenon of the leadership scenario among a group of people (a group of people being governed by a selected leader(s)). These leaders communicate in a meeting room, and the overall best leader makes the final decision. The simulation aspect of the study considered several benchmark functions and compared the performance of the suggested algorithm to that of the standard PSO (SPSO) in terms of efficiency.

Binary Accelerated Particle Swarm Algorithm for Discrete Optimization Problems

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Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems

Journal of Global Optimization, 2013

New Binary Particle Swarm Optimization with Immunity-Clonal Algorithm

Journal of Computer Science, 2013

Particle Swarm Optimization used to solve a continuous problem and has been shown to perform well however, binary version still has some problems. In order to solve these problems a new technique called New Binary Particle Swarm Optimization using Immunity-Clonal Algorithm (NPSOCLA) is proposed This Algorithm proposes a new updating strategy to update the position vector in Binary Particle Swarm Optimization (BPSO), which further combined with Immunity-Clonal Algorithm to improve the optimization ability. To investigate the performance of the new algorithm, the multidimensional 0/1 knapsack problems are used as a test benchmarks. The experiment results demonstrate that the New Binary Particle Swarm Optimization with Immunity Clonal Algorithm, found the optimum solution for 53 of the 58 multidimensional 0/1knapsack problems.

A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO (original) (raw)

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