A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

• Published in: Entropy (Basel, Switzerland) Vol. 21; no. 5; p. 494
• Main Authors: Li, Guocheng, Liu, Pei, Le, Chengyi, Zhou, Benda
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.05.2019; MDPI
• Subjects: Adaptive algorithms; Bionics; co-evolution; cross-entropy method; Design engineering; Entropy (Information theory); Evolution; Evolutionary algorithms; firefly algorithm; Genetic algorithms; Global optimization; Heuristic; Heuristic methods; Industrial design; Light; meta-heuristic; Methods; Optimization algorithms; Probability distribution; Robustness; Searching
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e21050494

Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.