Abstract
Cooperative coevolution (CC) is an effective framework for solving large-scale global optimization (LSGO) problems. However, CC with static decomposition method is ineffective for fully nonseparable problems, and CC with dynamic decomposition method to decompose problems is computationally costly. Therefore, a two-stage decomposition (TSD) method is proposed in this paper to decompose LSGO problems using as few computational resources as possible. In the first stage, to decompose problems using low computational resources, a hybrid-pool differential grouping (HPDG) method is proposed, which contains a hybrid-pool-based detection structure (HPDS) and a unit vector-based perturbation (UVP) strategy. In the second stage, to decompose the fully nonseparable problems, a known information-based dynamic decomposition (KIDD) method is proposed. Analytical methods are used to demonstrate that HPDG has lower decomposition complexity compared to state-of-the-art static decomposition methods. Experiments show that CC with TSD is a competitive algorithm for solving LSGO problems.
Highlights
Large-scale global optimization (LSGO) problems are widespread optimization problems in scientific research and engineering applications [1,2,3,4,5,6]
known information-based dynamic decomposition (KIDD) and hybrid-pool differential grouping (HPDG) together constitute the two-stage decomposition (TSD) method to improve the performance of Cooperative coevolution (CC) solving LSGO problems
Compared with the stateof-the-art DECC-efficient recursive differential grouping (ERDG), DECC-TSD is more advantageous on the fully nonseparable function f15. erefore, DECCTSD alleviates the dilemma of CC with static decomposition in solving the fully nonseparable problems
Summary
Large-scale global optimization (LSGO) problems are widespread optimization problems in scientific research and engineering applications [1,2,3,4,5,6]. Continuous LSGO problems with 1000 or greater decision variables are studied and solved. Ere are two main challenges in solving LSGO problems: (1) dimensional catastrophe [7] and (2) traditional mathematical algorithms and evolutionary algorithms (EAs) with rapid performance degradation as the dimensionality grows [8,9,10]. E first approach is to design or improve EAs with powerful exploration so that the EAs avoid getting trapped in local optima in large-scale solution space [11]. E second approach is the one studied in this paper CC has been used to solve a variety of problems [14,15,16]. e second approach is the one studied in this paper
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