A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System (original) (raw)
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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.
2009
Over the years, the area of Artificial Immune Systems (AIS) has drawn wide attention among researchers as the algorithm is able to enhance local searching ability and efficiency. Alternatively, Particle Swarm Optimization (PSO) has been used effectively in solving optimization problems. This paper compares the optimization results of the mathematical functions using AIS and PSO. The numerical results show that both PSO and AIS give comparable fitness solutions with the former performing about 56 percent faster than the latter. Conversely, for simpler mathematical functions, AIS performs marginally faster than PSO at about 14 percent while maintaining good accuracy of the objective value.
Evolutionary artificial immune system optimization
Proceedings of the …, 2010
In this paper, it is presented a new evolutionary algorithm of advanced optimization based in the technique of Artificial Immune Systems and in the Principles of Game Theory, more specifically, in the inclusion of evolutionary characteristics and a phase called "Social Interaction" in the algorithm AISO. In this way some theoretical aspects are presented, the new algorithm proposal and finally some simulation and comparison to Classical Genetic Algorithm and Genetic Algorithm with Social Interaction are made to the Traveling Salesman Problem.
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.
A New Discrete Particle Swarm Optimization Algorithm
Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016
Particle Swarm Optimization (PSO) has been shown to perform very well on a wide range of optimization problems. One of the drawbacks to PSO is that the base algorithm assumes continuous variables. In this paper, we present a version of PSO that is able to optimize over discrete variables. This new PSO algorithm, which we call Integer and Categorical PSO (ICPSO), incorporates ideas from Estimation of Distribution Algorithms (EDAs) in that particles represent probability distributions rather than solution values, and the PSO update modifies the probability distributions. In this paper, we describe our new algorithm and compare its performance against other discrete PSO algorithms. In our experiments, we demonstrate that our algorithm outperforms comparable methods on both discrete benchmark functions and NK landscapes, a mathematical framework that generates tunable fitness landscapes for evaluating EAs.
Hybrid Artificial Immune System-Genetic Algorithm optimization based on mathematical test functions
2010 IEEE Student Conference on Research and Development (SCOReD), 2010
This paper demonstrates a hybrid between two optimization methods that are Artificial Immune System (AIS) and Genetic Algorithm (GA). The capability of overcoming the shortcomings of individual algorithms without losing their advantages makes the hybrid techniques superior to the stand-alone ones based on the dominant purpose of hybridization. The improvement of the results that enable to get it if GA and AIS work separately is the main objective of this hybrid. The hybrid includes two processes; firstly, AIS is the attraction among the researchers as the algorithm. This enables it to develop local searching ability and efficiency yet the convergence rate for AIS is preferably not precise compared to the GA. Secondly, a Genetic Algorithm is typically initializing population randomly. The last generation of AIS will be the input to the next process of the hybrid which is the GA in this hybrid AIS-GA. Hybrid makes GA enters the stage of standard solutions more rapidly and more accurate compared with GA initialized population at random. To differentiate between the results in terms of achieving the minimum value for these functions, eight mathematical test functions are being used to make comparison.
2021
One of the heuristic optimization methods that implements the simulation of an artificial immune system is discussed in this article. The list of optimization problems, their classification and known approaches of solving those ones are reviewed on the whole. The research of the main settings of operators in the proposed algorithm is applied to the different dimensions test problems. For the first time, an attempt of finding a universal approach of solving both combinatorial and continuous problems, applying the proposed algorithm, was implemented based on the common approach to the algorithm operators. The most efficient settings, based on the minimum solution search time criteria, are recommended for agent generation search, parental individuals selection, mutation, crossover, local search and population compression operators.
A Hybrid Artificial Immune Optimization Method
International Journal of Computational Intelligence Systems, 2009
This paper proposes a hybrid optimization method based on the fusion of the Simulated Annealing (SA) and Clonal Selection Algorithm (CSA), in which the SA is embedded in the CSA to enhance its search capability. The novel optimization algorithm is also employed to deal with several nonlinear benchmark functions as well as a practical engineering design problem. Simulation results demonstrate the remarkable advantages of our approach in achieving the diverse optimal solutions and improved convergence speed.
Clonal particle swarm optimization and its applications
2007
Abstract Particle swarm optimization (PSO) is a stochastic global optimization algorithm inspired by social behavior of bird flocking in search for food, which is a simple but powerful, and widely used as a problem-solving technique to a variety of complex problems in science and engineering. A novel particle swarm optimization algorithm based on immunity-clonal strategies, called as clonal particle swarm optimization (CPSO), is proposed at first in this paper.
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
Do Artificial Immune Systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the Bcell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledge that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.