Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Particle Swarms for Linearly Constrained Optimisation
Fundamenta Informaticae
A co-evolutionary approach for military operational analysis
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Evolutionary algorithms for minimax problems in robust design
IEEE Transactions on Evolutionary Computation
A memory-based colonization scheme for particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm optimization driven by evolving elite group
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-swarm particle swarm optimiser with Cauchy mutation for dynamic optimisation problems
International Journal of Innovative Computing and Applications
Multi-objective PSO based on evolutionary programming
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Expert Systems with Applications: An International Journal
A differential evolution approach for solving constrained min-max optimization problems
Expert Systems with Applications: An International Journal
Particle Swarms for Linearly Constrained Optimisation
Fundamenta Informaticae
Taguchi-Particle Swarm Optimization for Numerical Optimization
International Journal of Swarm Intelligence Research
Research and analysis on ionospheric composition based on particle swarm optimization
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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A co-evolutionary particle swarm optimization (PSO) to solve constrained optimization problems is proposed. First, we introduce the augmented Lagrangian to transform a constrained optimization to a min-max problem with the saddle-point solution. Next, a co-evolutionary PSO algorithm is developed with one PSO focusing on the minimum part of the min-max problem with the other PSO focusing on the maximum part of the min-max problem. The two PSOs are connected through the fitness function. In the fitness calculation of one PSO, the other PSO serves as the environment to that PSO. The new algorithm is tested on three benchmark functions. The simulation results illustrate the efficiency and effectiveness of the new co-evolutionary particle swarm algorithm.