Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.

Multi-Population Coevolutionary Differential Evolution Algorithm

The multi-population coevolutionary differential evolution (DE) based on estimation of distribution algorithm (EDA) is proposed. DE completes optimum search using the difference information between the individuals in the population, but the global population evolution information can not be used sufficiently. In this paper, the multi-population co-evolutionary is introduced, which incorporate the merits of the DE and EDA. The elite mutation is proposed in DE. To overcome the greed characteristic, the chaotic initialization and replacement are introduced in DE and the individual diversity in EDA is adjusted based on the individual density. Simulation results show the good global search ability of the proposed algorithm.