A Hybrid Multi-Agent-Based BFPSO Algorithm for Optimization of Benchmark Functions

Abstract

The goal to provide faster and optimal solution to complex and high-dimensional problem is pushing the technical envelope related to new algorithms. While many approaches use centralized strategies, the concept of multi-agent systems (MASS) is creating a new option related to distributed analyses for the optimization problems. A novel learning algorithm for solving the global numerical optimization problems is proposed. The proposed learning algorithm integrates the multi-agent system and the hybrid butterfly–particle swarm optimization (BFPSO) algorithm. Thus it is named as multi-agent-based BFPSO (MABFPSO). In order to obtain the optimal solution quickly, each agent competes and cooperates with its neighbors and it can also learn by using its knowledge. Making use of these agent–agent interactions and sensitivity and probability mechanism of BFPSO, MABFPSO realizes the purpose of optimizing the value of objective function. The designed MABFPSO algorithm is tested on specific benchmark functions. Simulations of the proposed algorithm have been performed for the optimization of functions of 2, 20 and 30 dimensions. The comparative simulation results with conventional PSO approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low-and high-dimensional functions. The optimization strategy is general and can be used to solve other power system optimization problems as well.