Artificial Intelligence
Chaotic dynamic characteristics in swarm intelligence
Applied Soft Computing
The landscape adaptive particle swarm optimizer
Applied Soft Computing
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
Multiobjective optimization using population-based extremal optimization
Neural Computing and Applications
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A poly-hybrid PSO optimization method with intelligent parameter adjustment
Advances in Engineering Software
Particle swarm algorithm with hybrid mutation strategy
Applied Soft Computing
A population-based hybrid extremal optimization algorithm
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Damage detection based on improved particle swarm optimization using vibration data
Applied Soft Computing
Information Sciences: an International Journal
Sensor deployment for fault diagnosis using a new discrete optimization algorithm
Applied Soft Computing
Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO)
Applied Soft Computing
Review: A parameter selection strategy for particle swarm optimization based on particle positions
Expert Systems with Applications: An International Journal
Computers and Operations Research
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO-EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. We testify the performance of the proposed approach on a suite of unimodal/multimodal benchmark functions and provide comparisons with other meta-heuristics. The proposed approach is shown to have superior performance and great capability of preventing premature convergence across it comparing favorably with the other algorithms.