Abstract
For large scale global optimization (LSGO) problems, many algorithms have been proposed in recent years. However, there are still some issues to be further handled since the search space grows exponentially and the problem solving becomes more and more difficult as the problem scale becomes larger and larger. In this paper, we propose a new hybrid algorithm for solving large-scale global optimization problems. First, we adopt an existing group algorithm to divide the large-scale problem into several small-scale problems. Second, a modified self-adaptive discrete scan method is designed to roughly scan the whole search space and then focus the search on the promising regions. Third, a hybrid search strategy is proposed, which adaptively chooses the one-dimensional search scheme or the covariance matrix adaptation evolutionary strategy to solve the subproblems of separable, partially (additively) separable problems or non-separable problems, respectively. To demonstrate the performance of the proposed algorithm, we conduct the experiments on 15 difficult LSGO problems in CEC'2013 benchmark suite and compare the performance of the proposed algorithm with that of the several state-of-the-art algorithms. The results show that the proposed algorithm is more effective than the compared algorithms in terms of solution accuracy.
Highlights
Many real world problems can be modeled as continuous large scale global function optimization problems, and these problems appear in a variety of fields such as biometrics, aerospace, network antennas, data mining and so on [1]–[4]
The average ranking of mSaDS-combination optimization algorithm (COA) is 1.7, the smallest one among the four compared algorithms. This result means that the comprehensive performance of mSaDS-COA is better than multiple offspring sampling (MOS), CMAESCC-RDG2, and TPHA on solving large scale optimization problems, which is consistent with the aforementioned analysis
In this paper, we propose a new hybrid algorithm for large scale global optimization (LSGO) problems based on contribution-based Cooperative Co-evolution (CC) framework
Summary
Many real world problems can be modeled as continuous large scale global function optimization problems, and these problems appear in a variety of fields such as biometrics, aerospace, network antennas, data mining and so on [1]–[4]. We propose a new hybrid algorithm based on a modified self-adaptive discrete scan method and a sub-problem solving strategy called the combination optimization strategy. A strategy combining the covariance matrix adaption evolution strategy (CMAES) with one-dimensional search method to optimize the subcomponent is designed to improve the performance of the hybrid algorithm. In the second phase (i.e. optimization phase), the modified self-adaptive discrete scan method (mSaDS) proposed in Section III-A is used to scan the whole search space quickly and roughly, and narrow it to a promising region. B. A COMBINATION OPTIMIZATION ALGORITHM After scanning the entire search space by mSaDS, we can locate promising regions and find good solutions for LSGO problems. In the following optimization step, we propose a combination optimization algorithm (COA) based on the characteristics of one-dimensional search methods, CMAES and CBCC for solving LSGO problems.
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