Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Frankenstein's PSO: a composite particle swarm optimization algorithm
IEEE Transactions on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast multi-swarm optimization with cauchy mutation and crossover operation
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A New Particle Swarm Algorithm and Its Globally Convergent Modifications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
DNA Sequence Compression Using Adaptive Particle Swarm Optimization-Based Memetic Algorithm
IEEE Transactions on Evolutionary Computation
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The Particle Swarm Optimization (PSO) is a simple, yet very effective, population-based search algorithm. However, degradation of the population diversity in the late stages of the search, or stagnation, is the PSO's major drawback. Most of the related recent research efforts are concentrated on alleviating this drawback. The direct solution to this problem is to introduce modifications which increase exploration; however it is difficult to maintain the balance of exploration and exploitation of the search process with this approach. In this paper we propose the decoupling of exploration and exploitation using a team-oriented search. In the proposed algorithm, the swarm is divided into two independent teams or sub swarms; each dedicated to a particular aspect of search. A simple but effective local search method is proposed for exploitation and an improvised PSO structure is used for exploration. The validation is conducted using a wide variety of benchmark functions which include shifted and rotated versions of popular test functions along with recently proposed composite functions and up to 1000 dimensions. The results show that the proposed algorithm provides higher quality solution with faster convergence and increased robustness compared to most of the recently modified or hybrid algorithms based on PSO. In terms of algorithm complexity, TOSO is slightly more complex than PSO but much less complex than CLPSO. For very high dimensions (D400), however, TOSO is the least complex compared to both PSO and CLPSO.