Abstract
Nature-inspired algorithms serve as the backbone of modern computing technology, and over the past three decades, the field has grown enormously. Many applications were solved by such algorithms and are replacing the traditional classical optimization processes. A recent naked mole-rat algorithm (NMRA) was proposed based on the mating patterns of naked mole-rats. This algorithm proved its worth in terms of competitiveness and application to various domains of research. The aim was to propose an algorithm based on NMRA, named enhanced NMRA (ENMRA), by mitigating the problems that this algorithm suffers from: slow convergence, poor exploration, and local optima stagnation. To enhance the exploration capabilities of basic NMRA, grey wolf optimization (GWO)-based search equations were employed. Exploitation was improved using population division methods based on local neighborhood search (LNS) and differential evolution (DE) equations. To avoid the local stagnation problem, a neighborhood search strategy around the best individual was utilized. Such improvements help the new variant to solve highly challenging optimization problems in contrast to existing algorithms. The efficacy of ENMRA was evaluated using CEC 2019 benchmark test suite. The results were statistically analyzed by the Wilcoxon rank-sum test and Friedman rank (f-rank) test. The resulting analysis proved that ENMRA is superior to the competitive algorithms for test functions CEC 2019 with overall effectiveness of 60.33%. Moreover, the real-world optimization problem from underground wireless sensor networks for an efficient cross-layer solution was also used to test the efficiency of ENMRA. The results of comparative study and statistical tests affirmed the efficient performance of the proposed algorithm.
Highlights
In the present world, there are numerous real-world applications of optimization.Currently, many scientific fields and industrial companies face many hurdles in finding an appropriate optimization tool or, more precisely, an optimization algorithm to solve realworld problems
This work enhanced the performance of the optimization algorithm based on the This work enhanced the performance of the optimization algorithm based on the mating behavior of naked mole-rats (NMRs)
The proposed enhanced NMRA (ENMRA) used enhanced exploration and exploitation properties, concepts of hybridization, and a division of population strategy to imtation properties, concepts of hybridization, and a division of population strategy to improve the performance of a basic naked mole-rat algorithm (NMRA) optimizer
Summary
There are numerous real-world applications of optimization. Currently, many scientific fields and industrial companies face many hurdles in finding an appropriate optimization tool or, more precisely, an optimization algorithm to solve realworld problems. It was found that FPA suffers from the local optima stagnation problem and slow convergence [7,8], and the requirement of a new enhanced version of such algorithms is needed. NIAs suffer from these problems, and there is a requirement to design new algorithms or improve the existing algorithms for prospective researchers In this respect, a substantial effort was made to eradicate the issues above by introducing new enhanced versions of nature-inspired algorithms using new and novel strategies. This work dealt with the proposal to enhance the exploitation and exploration characteristics of a new algorithm, namely NMRA, to solve CEC 2019 benchmark problems and some real-world applications, such as an energy-throughput efficient, cross-layer solution for underground wireless sensor networks.
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