The purpose of this paper is to show, on a widely used benchmark problem, that normative knowledge concepts can be incorporated into particle swarm optimization (PSO) algorithms in order to improve their search ability.
Normative knowledge concepts are used within the framework of PSO algorithms in order to influence the cognitive and social components of the particle behaviour.
It is shown that the proposed algorithm can significantly improve the performance of PSO on the selected benchmark problem, in terms of both best and average solutions.
Although the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.
The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.
This paper introduces the use of normative knowledge concepts to control the cognitive and social components of PSO algorithms.
