Cooperative Coevolutionary Particle Swarms using Fuzzy Logic for Large Scale Optimization

Abstract

A cooperative coevolutionary framework can improve the performance of optimization algorithms on large-scale problems. In this paper, we propose a new Cooperative Coevolutionary Coevolutionary algorithm based on our preliminary work FuzzyPSO2. This new proposal, called CCFPSO, uses a variable decomposition method, adopting the random grouping technique and a dynamic subcomponent size at each generation. Unlike FuzzyPSO2, in CCFPSO the re-initialization of the variables suggested by the fuzzy system is performed on the particles that has the worst fitness value in each generation. Moreover, the particles are updated based on their best position and its neighborhoods, instead of the best particle in the population as its standard version. On high-dimensional problems that more closely resemble real-world problems (CEC2008, CEC2010) the performance of CCFPSO is favorable compared to other state-of-the-art PSO versions such as CCPSO2, SLPSO and CSO. The results indicate that using a Cooperative Coevolutionary PSO approach with a fuzzy logic system can improve results on high dimensionality problems (100 to 1000 variables).

Keywords

• adaptive inertia weight
• cooperative coevolutionary
• fuzzy logic
• particle swarm optimization