Abstract
Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. However, difficulties in scalability upon MaOPs including the selection of leaders, etc., are encountered because the most evolutionary algorithms are proposed for single-objective optimization. To further improve the performance of many-objective evolutionary algorithms in solving MaOPs when the number of the objectives increases, this paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). In MOIFF, a novel bio-inspired meta heuristic method proposed in 2018, called Farmland Fertility algorithm (FF), is employed to serve as the optimization strategy. 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; a novel method based on individual cumulative ranking value to constitute and update the global memory and local memory of each individual is proposed, 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 MaOPs.
Highlights
Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed
In order to handle MaOPs effectively, Farmland Fertility algorithm (FF) has been tailored from the following aspects in this study: First, we propose a novel individual fitness assessment approach based on the cumulative ranking value to distinguish the advantages and disadvantages of each individual in MaOPs
According to the characteristics of MaOPs, we proposed a novel method based on individual cumulative ranking value to constitute and update the global memory and local memory of each individual, and propose a hybrid search mode combining subspace search and full space search to update individuals at the stages of soil optimization and soil fusion
Summary
Recent studies on many-objective optimization problems (MaOPs) have tended to employ some promising evolutionary algorithms with excellent convergence accuracy and speed. Difficulties in scalability upon MaOPs including the selection of leaders, etc., are encountered because the most evolutionary algorithms are proposed for single-objective optimization. To further improve the performance of many-objective evolutionary algorithms in solving MaOPs when the number of the objectives increases, this paper proposes a many-objective optimization algorithm based on the improved Farmland Fertility algorithm (MOIFF). MOPs have not been well solved when the number of objectives increases To overcome this challenge, lots of many-objective evolutionary algorithms which adopted an evolutionary algorithm, have been proposed to improve the performance in solving MaOPs. most evolutionary algorithms solve single-objective optimization problems. Dabba et al applied the artificial fish swarm optimization algorithm to solve the MaOPs12
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