Introduction to parallel algorithms and architectures: array, trees, hypercubes
Introduction to parallel algorithms and architectures: array, trees, hypercubes
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Pseudocoevolutionary genetic algorithms for power electronic circuits optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
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This paper proposes a parallel particle swarm optimization (PPSO) by dividing the search space into sub-spaces and using different swarms to optimize different parts of the space. In the PPSO framework, the search space is regarded as a solution vector and is divided into two sub-vectors. Two cooperative swarms work in parallel and each swarm only optimizes one of the sub-vectors. An adaptive asynchronous migration strategy (AAMS) is designed for the swarms to communicate with each other. The PPSO benefits from the following two aspects. First, the PPSO divides the search space and each swarm can focus on optimizing a smaller scale problem. This reduces the problem complexity and makes the algorithm promising in dealing with large scale problems. Second, the AAMS makes the migration adapt to the search environment and results in a very timing and efficient communication fashion. Experiments based on benchmark functions have demonstrated the good performance of the PPSO with AAMS on both solution accuracy and convergence speed when compared with the traditional serial PSO (SPSO) and the PPSO with fixed migration frequency.