Natural Survivor Method (NSM) for Metaheuristic Algorithms

Natural Survivor Method (NSM) for Designing an Updating Mechanism in Metaheuristic Search Algorithms The Best SOTA Optimization Algorithms
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Updated 15 Mar 2023

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Build an enhanced version of your meta-heuristic search algorithm using the NSM-based survivor selection mechanism. You can use the NSM method to design the update mechanism in the MHS algorithm. The method is very easy to implement. You can use different strategies in applying the NSM method. In this way, you can design the version of your algorithm that is most effective on your optimization problem. In a way, this can be thought of as fine-tuning, adapting the algorithm for the optimization problem. We introduced alternative switching strategies to implement NSM. We explained them in the article. We recommend using the NSM method in your algorithm designs.
We also tested NSM on three different MHS algorithms and improved the convergence performance of all three. We have shared the source codes here. We recommend combining NSM and fitness-based survival in the search process lifecycle. We hope that the NSM-TLABC, NSM-LSHADE-SPACMA and NSM-SFS algorithms will find the optimum solutions for your problems.
Please read the article for details.
Abstract
Meta-heuristic search algorithms (MHSs) are methods that take their inspiration from nature. However, the fitness value information used in the design of the update mechanism in MHSs is insufficient to represent the concept of adaptation to the environment and the ability to survive in nature. This causes problems in the selection of survivors and the premature convergence in the search process. This article introduces the Natural Survivor Method (NSM), developed as a design for population management as it occurs in nature, depending on analytical relationships and environmental factors. In the NSM, scores representing the adaptability of individuals to nature are calculated in order to determine the survivors. In this proposed method, the update mechanism is designed using NSM scores instead of fitness values. The NSM is the first study presented to the literature on this subject since the 1980s, when the meta-heuristics was introduced. The NSM was used for survivor selection by applying it to three different types of MHS algorithms based on physics (SFS), biology (TLABC), and evolution (LSHADE-SPACMA). Thirty-nine global optimization problems in the IEEE CEC 2017/2020 benchmark suites and ten constrained real-world engineering problems were used in the experimental studies. Data obtained from experimental studies were analyzed by using non-parametric statistical test methods. Among the 25 competing algorithms according to Friedman scores, the rankings of the three algorithms with NSM and their original versions are as follows: While TLABC is 18th, NSM-TLABC is 9th, SFS is 10th, NSM-SFS is 6th, LSHADE-SPACMA is 3rd, NSM-LSHADE-SPACMA is 1st. According to the results of the Wilcoxon pairwise comparison test between the original and NSM versions of the algorithms, the NSM versions have a clear advantage in finding optimal solutions. However, the drawback of the proposed method is that it increases the computational complexity of the algorithms.

Cite As

Kahraman, H. T., Katı, M., Aras, S., Taşci, D. A. (2022). Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms. Engineering Applications of Artificial Intelligence, 10.1016/j.engappai.2023.106121, 122, 106121.

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