Abstract
Atom search optimization algorithm has good searching ability and has been successfully applied to calculate hydrogeological parameters and groundwater dispersion coefficient. Since the atom search optimization algorithm is only based on the atom force motion model in molecular dynamics, it has some shortcomings such as slow search speed and low precision during the later stage of iteration. A modified atom search optimization based on the immunologic mechanism and reinforcement learning is proposed to overcome the abovementioned shortcomings in this paper. The proposed algorithm introduces a vaccine operator to better utilize the dominant position in the current atom population so that the speed, accuracy, and domain search ability of the atom search optimization algorithm can be strengthened. The reinforcement learning operator is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability. The test results of 21 benchmark functions confirm that the performance of the proposed algorithm is superior to seven contrast algorithms in search accuracy, convergence speed, and robustness. The proposed algorithm is used to optimize the permutation flow shop scheduling problem. The experimental results indicate that the proposed algorithm can achieve better optimization results than the seven comparative algorithms, so the proposed algorithm has good practical application value.
Highlights
Optimization has been a hot topic in scientific research and engineering application [1] such as industry, society, economy, and management. e optimization methods include traditional exact solution method, constructive algorithm, and swarm intelligence algorithm [2]. e traditional exact solution method can obtain the exact solution, but it requires the objective function to be continuous and differentiable. e algorithm of traditional exact solution method is too complex, so it is suitable for solving smallscale problems
Since atom search optimization (ASO) is proposed only based on the forced motion model of atoms in molecular dynamics, it has the shortcomings of low search accuracy and slow convergence speed as other swarm intelligence algorithms
Simulation Experiment e performance of mechanism and reinforcement learning (MASO) will be veri ed by experiments in three aspects: (1) e MASO will compare with seven swarm intelligence algorithms using 21 benchmark functions [39,40,41] of 30 dimensions. (2) e signi cance of MASO improvement will be analyzed by the T test. (3) e signi cance of MASO improved by individual strategies will be analyzed with the Wilcoxon rank sum test
Summary
Optimization has been a hot topic in scientific research and engineering application [1] such as industry, society, economy, and management. e optimization methods include traditional exact solution method, constructive algorithm, and swarm intelligence algorithm [2]. e traditional exact solution method can obtain the exact solution, but it requires the objective function to be continuous and differentiable. e algorithm of traditional exact solution method is too complex, so it is suitable for solving smallscale problems. E optimization methods include traditional exact solution method, constructive algorithm, and swarm intelligence algorithm [2]. The traditional exact solution method is not good at optimizing multipeak, nonlinear, and dynamic problems. E swarm intelligence algorithm provides a solution to complex problems without centralized control and without providing the global mathematical model. Compared with traditional optimization methods, the swarm intelligence algorithm has simple principle and fewer parameters and does not need gradient information of the problem. Since ASO is proposed only based on the forced motion model of atoms in molecular dynamics, it has the shortcomings of low search accuracy and slow convergence speed as other swarm intelligence algorithms. Inspired by the above facts, the modified atom search optimization algorithm based on immunologic mechanism and reinforcement learning (MASO) is proposed to enhance the ASO. Inspired by the above facts, the modified atom search optimization algorithm based on immunologic mechanism and reinforcement learning (MASO) is proposed to enhance the ASO. e immunologic mechanism can make better use of the dominant positions in the current atomic population to promote the speed and accuracy of the MASO. e reinforcement learning is applied to dynamically adjust the vaccination probability to balance the global exploration ability and local exploitation ability of the MASO
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