An Improved Farmland Fertility Algorithm For Many-Objective Optimization Problems

Abstract

Recent studies in many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, they will encounter difficulties in their scalability upon MaOPs because the most evolutionary algorithms are proposed for single-objective optimization, which will affect the selection of leaders and individual-updating mechanisms, thereby deteriorating the effectiveness of the algorithm. This paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). A novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy of MOIFF. In order to handle MaOPs effectively, FF has been tailored from the following aspects: an individual fitness assessment approach based on cumulative ranking value has been proposed to distinguish the quality of each individual, updated the global memory and local memory of each individual based on individual cumulative ranking value, and a hybrid subspace search and full space search method has been designed to update individuals in the stages of soil optimization and soil fusion. In addition, adaptive environmental selection has been proposed. Finally, MOIFF is compared with four state-of-the art many-objective evolutionary algorithms on many test problems with various characteristics, including the DTLZ and WFG test suites. Experimental results demonstrate that the proposed algorithm has competitive convergence and diversity on many-objective optimization problems.