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A Hybrid Particle Swarm Optimization Algorithm for Function Optimization
2008In this paper, a new variation of Particle Swarm Optimization (PSO) based on hybridization with Reduced Variable Neighborhood Search (RVNS) is proposed. In our method, general flow of PSO is preserved. However, to rectify premature convergence problem of PSO and to improve its exploration capability, the best particle in the swarm is randomly re ...
Aise Zülal Sevkli +1 more
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Evolving the Structure of the Particle Swarm Optimization Algorithms
2006A new model for evolving the structure of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The evolved PSO algorithm is compared to a human-
Laura Diosan, Mihai Oltean
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Grey-Based Particle Swarm Optimization Algorithm
2012In order to apply grey relational analysis to the evolutionary process, a modified grey relational analysis is introduced in this study. Then, with the help of such a grey relational analysis, this study also proposed a grey-based particle swarm optimization algorithm in which both inertia weight and acceleration coefficients are varying over the ...
Ming-Feng Yeh +2 more
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Parameter Evolution for a Particle Swarm Optimization Algorithm
2010Setting appropriate parameters of an evolutionary algorithm (EA) is challenging in real world applications. On one hand, the characteristics of a real world problem are usually unknown. On the other hand, in different running stages of an EA, the best parameters may be different. Thus adaptively tuning algorithm parameters online is preferred.
Aimin Zhou +2 more
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Expand-and-Reduce Algorithm of Particle Swarm Optimization
2008This paper presents an optimization algorithm: particle swarm optimization with expand-and-reduce ability. When particles are trapped into a local optimal solution, a new particle is added and the trapped particle(s) can escape from the trap. The deletion of the particle is also used in order to suppress excessive network grows.
Eiji Miyagawa, Toshimichi Saito
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Headless Chicken Particle Swarm Optimization Algorithms
2016This paper investigates various strategies for implementing the headless chicken macromutation operator in the particle swarm optimization domain. Three different headless chicken particle swarm optimization algorithms are proposed and evaluated against a standard guaranteed convergence PSO algorithm on a diverse set of benchmark problems.
Jacomine Grobler, Andries P. Engelbrecht
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A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing, 2010The canonical particle swarm optimization (PSO) has its own disadvantages, such as the high speed of convergence which often implies a rapid loss of diversity during the optimization process, which inevitably leads to undesirable premature convergence.
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A Modified Adaptive Particle Swarm Optimization Algorithm
2016 12th International Conference on Computational Intelligence and Security (CIS), 2016Particle swarm optimization (PSO) is a heuristic stochastic evolutionary algorithm. However, standard PSO exists unbalanced exploitation and exploration, lower convergence speed. An improved technique is introduced into the standard PSO with adaptive computation of the inertia weights.
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A dynamic mutation particle swarm optimization algorithm
Proceedings of the Conference on Research in Adaptive and Convergent Systems, 2022Yang Zhang 0093 +2 more
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Improvement and application of particle swarm optimization algorithm
Intelligent Decision TechnologiesParticle Swarm Optimization (PSO) remains straightforward and has many scientific and engineering applications. Most real-world optimization problems are nonlinear and discrete with local constraints. The PSO algorithm encounters issues such as inefficient solutions and early convergence.
Durga Praveen Deevi +5 more
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