Opposition-based multi-objective whale optimization algorithm with global grid ranking

Nature-inspired computing has attracted a lot of research effort especially for addressing real-world multi-objective optimization problem (MOP). This paper proposes a new nature-inspired optimization algorithm which is named opposition-based multi-objective whale optimization algorithm with global grid ranking (MOWOA). The proposed approach utilizes several parts to enhance the performance in optimization. First, the efficient evolution process is inherited from the single objective whale optimization algorithm(WOA). Second, opposition-based learning(OBL) is applied into the algorithm. Meanwhile, a novel mechanism called global grid ranking(GGR) which is inspired by grid mechanism has been incorporated into the proposed algorithm. To show the significance of the proposed algorithm, MOWOA is tested on a diverse set of benchmark with a series of well-known evolutionary algorithms and the influence of each individual strategy is also verified through 14 benchmarks. Moreover, the new proposed algorithm is also applied to the simple data clustering problem and a real-world water optimization problem in China. The results demonstrate that MOWOA is not only an algorithm with well performance for bench-mark problems but also expected to have a more wide application in real-world engineering problems. (C) 2019 Elsevier B.V. All rights reserved.