Multi-objective whale optimization

• Published in: TENCON ... IEEE Region Ten Conference pp. 2747 - 2752
• Main Authors: Kumawat, Ishwar Ram, Nanda, Satyasai Jagannath, Maddila, Ravi Kumar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Algorithm design and analysis; Benchmark testing; Convergence; IEEE Regions; MOEA/D; MOGWO; MOPSO; Pareto front; Pareto optimization; Whale Optimization Algorithm; Whales
• ISSN: 2159-3450
• DOI: 10.1109/TENCON.2017.8228329

This paper deals with designing a multi-objective algorithm based on recently proposed Whale Optimization Algorithm (WOA), termed as MOWOA. The original WOA algorithm is popular among the researchers due to the encircling moments of agents (Humpback whales) in the search space which provides proper balance among the exploration and exploitation, faster convergence and lessor number of parameters. The proposed multi-objective version posses all the above benefits of the original algorithm, in addition it reveals accurate convergence to the true Pareto fronts and maintain effective diversity among the solutions. The performance is demonstrated on six unconstrained bi-objective functions of IEEE CEC 2009. The obtained results are compared with that achieved by multi-objective Grey Wolf Optimization (MOGWO), multi-objective Particle Swarm Optimization (MOPSO), multi-objective Evolutionary Algorithm based on Decomposition(MOEA/D).