Opposition-based learning in shuffled frog leaping: An application for parameter identification

• We propose using the opposition-based learning strategy in the shuffled frog leaping.
• The proposed idea diversifies search moves of shuffled frog leaping.
• We propose four new versions of shuffled frog leaping algorithm.
• Experiments on parameter identification problems confirm a significantly better performance of our proposed algorithms.

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled frog leaping (SFL). The SFL divides a population into several memeplexes and then improves each memeplex in an evolutionary process. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the SFL which employ on one hand the OBL to accelerate the SFL without making premature convergence and on the other hand use the OBL strategy to diversify search moves of SFL. Four versions of SFL algorithm are proposed by incorporating the OBL and the SFL. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments are performed on parameter identification problems. The obtained results demonstrate that incorporating the opposition-based strategy and SFL performed in a proper way is a good idea to enhance performance of SFL. Two versions of opposition-based SFL outperform their pure competitor i.e., SFL in terms of all aspects on all problems but two other versions of SFL obtained a worse performance than the pure SFL. Also some performance comparisons of the proposed algorithms with some other algorithms reported in the literature confirm a significantly better performance of our proposed algorithms. Also in final part of the comparison study, a comparison of the proposed algorithm in this study in respect to other algorithms on CEC05 functions demonstrates a completely comparable performance of proposed algorithm.