A hybrid social spider optimization and genetic algorithm for minimizing molecular potential energy function
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The minimization of the molecular potential energy function is one of the most important real-life problems which can help to predict the 3D structure of the protein by knowing the steady (ground) state of the molecules of the protein. In this paper, we propose a new hybrid algorithm between the social spider algorithm and the genetic algorithm in order to minimize a simplified model of the energy function of the molecule. We call the proposed algorithm by hybrid social spider optimization and genetic algorithm (HSSOGA). The HSSOGA comprises of three main steps. In the first step, we apply the social spider optimization algorithm to balance between the exploration and the exploitation processes in the proposed algorithm. In the second step, we use the dimensionality reduction process and the population partitioning process by dividing the population into subpopulations and applying the arithmetical crossover operator for each subpopulation order to increase the diversity of the search in the algorithm. In the last steps, we use the genetic mutation operator in the whole population to avoid the premature convergence and avoid trapping in local minima. The combination of three steps helps the proposed algorithm to solve the molecular potential energy function with different molecules size, especially when the problem dimension \(D>100\) with powerful performance. We test it on 13 large-scale unconstrained global optimization problems to investigate its performance on these functions. In order to investigate the efficiency of the proposed HSSOGA, we compare HSSOGA against eight benchmark algorithms when we minimize the potential energy function problem. The numerical experiment results show that the proposed algorithm is a promising and efficient algorithm and can obtain the global minimum or near global minimum of the large-scale optimization problems up to 1000 dimension and the molecular potential energy function of the simplified model with up to 200 degrees of freedom faster than the other comparative algorithms.
KeywordsSocial spider optimization Genetic algorithm Molecular energy function Global optimization
We are grateful to the anonymous reviewers for constructive feedback and insightful suggestions which greatly improved this article. The research of the first author is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). The postdoctoral fellowship of the second author is supported by NSERC.
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Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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