An Improved Whale Optimization Algorithm with Adaptive Fitness-Distance Balance

Whale optimization algorithm (WOA) is a new bio-meta-heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness-distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill-conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state-of-the-art WOA variants and other four classic meta-heuristic algorithms.