Evolutionary Computation Algorithms (ECA) are conceived as alternative methods for solving complex optimization problems through the search for the global optimum. Therefore, from a practical point of view, the acquisition of multiple promissory solutions is especially useful in engineering, since the global solution may not always be realizable due to several realistic constraints. Although ECAs perform well on the detection of the global solution, they are not suitable for finding multiple optima in a single execution due to their exploration-exploitation operators. This paper proposes a new algorithm called Collective Electromagnetism-like Optimization (CEMO). Under CEMO, a collective animal behavior is implemented as a memory mechanism simulating natural animal dominance over the population to extend the original Electromagnetism-like Optimization algorithm (EMO) operators to efficiently register and maintain all possible Optima in an optimization problem. The performance of the proposed CEMO is compared against several multimodal schemes over a set of benchmark functions considering the evaluation of multimodal performance indexes typically found in the literature. Experimental results are statistically validated to eliminate the random effect in the obtained solutions. The proposed method exhibits higher and more consistent performance against the rest of the tested multimodal techniques.
- Collective animal behavior
- Collective electromagnetism-like mechanism optimization
- Evolutionary computation algorithms
- Multimodal optimization
ASJC Scopus subject areas
- Artificial Intelligence