Rezaei, F., Safavi, HR., Abd Elaziz, M., Abualigah, L., Mirjalili, S., Gandomi, AH., Diversity-Based Evolutionary Population Dynamics: A New Operator for Grey Wolf Optimizer. Processes

Abstract

Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature,which is the opposite of survival of the fittest. Although this method can improve the median ofthe whole population of the meta-heuristic algorithms, it suffers from poor exploration capability tohandle high-dimensional problems. This paper proposes a novel EPD operator to improve the searchprocess. In other words, as the primary EPD mainly improves the fitness of the worst individuals inthe population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainlyimproves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB-EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD.In this algorithm, the three most diversified individuals are first identified at each iteration, andthen half of the best-fitted individuals are forced to be eliminated and repositioned around thesediversified agents with equal probability. This process can free the merged best individuals locatedin a closed populated region and transfer them to the diversified and, thus, less-densely populatedregions in the search space. This approach is frequently employed to make the search agents explorethe whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shiftedclassical benchmark functions as well as 29 test problems included in the CEC2017 test suite, andfour constrained engineering problems. The results obtained by the proposal upon implemented onthe classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newlyproposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm(FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). Theexperiments demonstrate the significant superiority of the proposed algorithm when applied to amajority of the test functions, recommending the application of the proposed EPD operator to anyother meta-heuristic whenever decided to ameliorate their performance.

. https://doi.org/10.3390/pr10122615