Balancing the Degree of Exploration and Exploitation of Swarm Intelligence Using Parallel Computing

Abstract

The two basic search behaviors used in metaheuristic algorithms in general and swarm intelligence in particular are exploration and exploitation. Exploration refers to searching the unexplored area of the feasible region while exploitation refers to the search of the neighborhood of a promising region. The success of these algorithms highly depends on how these two search behaviors are balanced. Increasing the number of initial solutions indeed increase the performance of the algorithm over the expense of the runtime. Different exploration and exploitation adjustments are used in different metaheuristic algorithms. To well balance the degree of exploration and exploitation, which ultimately leads to finding good solutions, needs a careful implementation and sufficient runtime. However, the runtime is another issue which has been a bottleneck for different application, especially for high dimensional, highly constrained and expensive objective function cases. Using parallel computing to overcome this limitation has been a central research issue in the study of metaheuristic algorithms. The three basic parallelization approaches used are the parallel move model which is a master slave model where solutions will be distributed to different processors for updating, parallel multi start model which uses several updating methods for the solutions in parallel and move acceleration method, which refers to a parallel implementation with parallelizing the objective function. There is no specific parallelization used towards balancing of exploration and exploitation is mentioned. Hence, in this paper a parallel implementation of swarm intelligence in general and two algorithms, namely particle swarm optimization and prey predator algorithm, in particular will be discussed. The parallelization proposed is aimed to merge the parallel move model and the parallel multi start model with the aim of adjusting the degree of exploration and exploitation. Experimental study based on ten benchmark problems from constrained as well as unconstrained problems will be used for simulation. It will be shown that the parallelization with specific distribution of exploration and exploitation runs better and efficiently compared to the serial and also the parallel version without exporation/exploitation adjustment.