Journal ArticleDOI
A New Particle Swarm Algorithm and Its Globally Convergent Modifications
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A new mutation strategy is used, which makes it easier for particles in hybrid MRPSO (HMRPSO) to find the global optimum and which also seeks a balance between the exploration of new regions and the exploitation of the already sampled regions in the solution spaces.Abstract:
Particle swarm optimization (PSO) is a population-based optimization technique that can be applied to a wide range of problems. Here, we first investigate the behavior of particles in the PSO using a Monte Carlo method. The results reveal the essence of the trajectory of particles during iterations and the reasons why the PSO lacks a global search ability in the last stage of iterations. Then, we report a novel PSO with a moderate-random-search strategy (MRPSO), which enhances the ability of particles to explore the solution spaces more effectively and increases their convergence rates. Furthermore, a new mutation strategy is used, which makes it easier for particles in hybrid MRPSO (HMRPSO) to find the global optimum and which also seeks a balance between the exploration of new regions and the exploitation of the already sampled regions in the solution spaces. Thirteen benchmark functions are employed to test the performance of the HMRPSO. The results show that the new PSO algorithm performs much better than other PSO algorithms for each multimodal and unimodal function. Furthermore, compared with recent evolutionary algorithms, experimental results empirically demonstrate that the proposed framework yields promising search performance.read more
Citations
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Journal ArticleDOI
A Competitive Swarm Optimizer for Large Scale Optimization
Ran Cheng,Yaochu Jin +1 more
TL;DR: Empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.
Journal ArticleDOI
Genetic Learning Particle Swarm Optimization
TL;DR: A specific novel *L-PSO algorithm is proposed, using genetic evolution to breed promising exemplars for PSO, and under such guidance, the global search ability and search efficiency of PSO are both enhanced.
Journal ArticleDOI
Swarm Intelligence Approaches to Optimal Power Flow Problem With Distributed Generator Failures in Power Networks
TL;DR: This work for the first time formulates an optimal power flow problem by considering controllable and uncontrollable distributed generators in power networks and finds its power output solution via particle swarm optimization and group search optimizer for coping with the difficult scenarios in a power network.
Journal ArticleDOI
A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques
TL;DR: A new adaptive inertia weight adjusting approach is proposed based on Bayesian techniques in PSO, which is used to set up a sound tradeoff between the exploration and exploitation characteristics and is compared with other types of improved PSO algorithms, which also performs well.
Journal ArticleDOI
Phasor particle swarm optimization: a simple and efficient variant of PSO
TL;DR: The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature.
References
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