Focus Group: An Optimization Algorithm Inspired by Human Behavior

Abstract

This paper presents a novel optimization algorithm, namely focus group (FG) algorithm, for solving optimization problems. The proposed algorithm is inspired by the behavior of group members to share their ideas (solutions) about a specific subject and trying to improve the solutions based on the cooperation and discussion. In the proposed algorithm, all the members present their solutions about the subject and all the suggested solutions proportional to their fitness, leave impact on the other solutions and incline them towards themselves. While trying to improve the quality of the candidate solutions, they converge to the optimal solution. To improve the performance of the proposed algorithm, two genetic operators are incorporated into the algorithm. The proposed algorithm is evaluated using several constrained and unconstrained benchmark functions commonly used in the area of optimization. Experimental results, in comparison with different well-known evolutionary techniques, confirm the high performance of the proposed algorithm in dealing with the optimization problems.