Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Center particle swarm optimization
Neurocomputing
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
An analysis of the effects of population structure on scalable multiobjective optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
Evolutionary dynamics on scale-free interaction networks
IEEE Transactions on Evolutionary Computation
Software engineering and scale-free networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiobjective evolutionary algorithms on complex networks
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Effects of scale-free and small-world topologies on binary coded self-adaptive CEA
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
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
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
The Self-Organization of Interaction Networks for Nature-Inspired Optimization
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A cooperative particle swarm optimizer with statistical variable interdependence learning
Information Sciences: an International Journal
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
Function optimisation by learning automata
Information Sciences: an International Journal
Topology of social networks and efficiency of collective intelligence methods
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This paper proposes a novel PSO algorithm, referred to as SFIPSO (Scale-free fully informed particle swarm optimization). In the proposed algorithm a modified Barabasi-Albert (BA) model [4] is used as a self-organizing construction mechanism, in order to adaptively generate the population topology exhibiting scale-free property. The swarm population is divided into two subpopulations: the active particles and the inactive particles. The former fly around the solution space to find the global optima; whereas the latter are iteratively activated by the active particles via attaching to them, according to their own degrees, fitness values, and spatial positions. Therefore, the topology will be gradually generated as the construction process and the optimization process progress synchronously. Moreover, the cognitive effect and the social effect on the variance of a particle's velocity vector are distributed by its ''contextual fitness'' value, and the social effect is further distributed via a time-varying weighted fully informed mechanism that originated from [27]. It is proved by the results of comparative experiments carried out on eight benchmark test functions that the scale-free population topology construction mechanism and the weighted fully informed learning strategy can provide the swarm population with stronger diversity during the convergent process. As a result, SFIPSO obtained success rate of 100% on all of the eight test functions. Furthermore, SFIPSO also yielded good-quality solutions, especially on multimodal test functions. We further test the network properties of the generated population topology. The results prove that (1) the degree distribution of the topology follows power-law, therefore exhibits scale-free property, and (2) the topology exhibits ''disassortative mixing'' property, which can be interpreted as an important condition for the reinforcement of population diversity.