Search Optimization Using Hybrid Particle Sub- Swarms and Evolutionary Algorithms

Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a variant of the PSO technique named Independent Neighborhoods Particle Swarm Optimization (INPSO) dealing with sub-swarms for solving the well known geometrical place problems. Finding the geometrical place can be sometimes a hard task and in almost all situations the geometrical place consists of more than one single point. Taking all these into account, the INPSO algorithm is very appealing for solving this class of problems. The performance of the INPSO approach is compared with Geometrical Place Evolutionary Algorithms (GPEA). The main advantage of the INPSO technique is its speed of convergence (finding quick solutions). To enhance the performance of the INPSO approach, a hybrid algorithm combining INPSO and GPEA is also proposed in this paper. The developed hybrid combination is able to detect the geometrical place much faster even for difficult problems for which the direct GPEA approach required more time and the INPSO (even with few sub-swarms) approach failed in finding all the geometrical place points (solutions).