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Beluga whale optimization (BWO) has received widespread attention in scientific and engineering domains. However, BWO suffers from limited adaptability, weak anti-stagnation, and poor exploration capability. Consequently, this study proposes an enhanced variant of BWO called multi-strategy improved beluga whale optimization (MIBWO). First, an improved ICMIC chaotic map is introduced to enhance exploration capability and optimization accuracy. Then, a dynamic parameter nonlinear adjustment strategy is integrated to achieve a better balance between exploration and exploitation. Finally, opposition learning based on the lens imaging principle is designed to strengthen anti-stagnation capability. An ablation experiment is performed to evaluate the impact of each strategy on the optimization capability of BWO. The experimental results demonstrate the significant enhancement in the performance of BWO owing to the used strategies. To further validate the performance of MIBWO, it is benchmarked against six state-of-the-art optimization algorithms using functions from CEC2005, CEC2014, and CEC2022. Statistical tests, including Friedman rank test and Wilcoxon rank-sum test, are performed. The experimental results show the superiority of MIBWO. Finally, MIBWO is applied to optimize 2D and 3D node coverage in wireless sensor networks and solve six constrained engineering problems. The experimental results indicate that MIBWO outperforms other competitors for practical engineering applications in terms of solution quality and convergence speed. [ABSTRACT FROM AUTHOR]

Artificial ecosystem-based optimization (AEO) is a nature-inspired intelligent optimization algorithm that has been widely applied to various real-world optimization problems. However, AEO has several limitations, including slow convergence and difficulty in escaping from local optima. To address these drawbacks, this study proposes an enhanced variant of AEO called enhanced artificial ecosystem-based optimization (EAEO). First, Latin hypercube sampling is introduced to achieve uniform population initialization. Then, a quadratic interpolation mechanism is embedded to accelerate convergence and improve accuracy. Finally, an adaptive neighborhood search inspired by animal migration behavior is designed to help to jump out of local optima. The performance of EAEO is evaluated using twenty-three benchmark functions and the CEC2017 test suite. The impact analysis, statistical analysis, and sensitivity analysis are performed. Experimental results indicate that EAEO outperforms the original AEO and other comparison algorithms in terms of accuracy and stability. Finally, the proposed EAEO is applied to address seven engineering optimization problems, and the results demonstrate the superiority of EAEO for global optimization tasks, constrained engineering problems, search performance, solution accuracy, and convergence speed. [ABSTRACT FROM AUTHOR]

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms.

Although the exploitation of GWO advances sharply, it has limitations for continuous implementing exploration. On the other hand, the EHO algorithm easily has shown its capability to prevent local optima. For hybridization and by considering the advantages of GWO and the abilities of EHO, it would be impressive to combine these two algorithms. In this respect, the exploitation and exploration performances and the convergence speed of the GWO algorithm are improved by combining it with the EHO algorithm. Therefore, this paper proposes a new hybrid Grey Wolf Optimizer (GWO) combined with Elephant Herding Optimization (EHO) algorithm. Twenty-three benchmark mathematical optimization challenges and six constrained engineering challenges are used to validate the performance of the suggested GWOEHO compared to both the original GWO and EHO algorithms and some other well-known optimization algorithms. Wilcoxon's rank-sum test outcomes revealed that GWOEHO outperforms others in most function minimization. The results also proved that the convergence speed of GWOEHO is faster than the original algorithms. [ABSTRACT FROM AUTHOR]

Remora Optimization Algorithm (ROA) is a recent population-based algorithm that mimics the intelligent traveler behavior of Remora. However, the performance of ROA is barely satisfactory; it may be stuck in local optimal regions or has a slow convergence, especially in high dimensional complicated problems. To overcome these limitations, this paper develops an improved version of ROA called Enhanced ROA (EROA) using three different techniques: adaptive dynamic probability, SFO with Levy flight, and restart strategy. The performance of EROA is tested using two different benchmarks and seven real-world engineering problems. The statistical analysis and experimental results show the efficiency of EROA. [ABSTRACT FROM AUTHOR]

Optimizing the high computational real-world problems is a challenging task that has taken a great deal of efforts in the last decade. The meta-heuristic algorithms have brought countless benefits. As a result, numerous meta-heuristic algorithms have been developed by getting inspired from natural phenomena. The atom search optimization (ASO) is a physics-based meta-heuristic, which has been developed little while ago. Although ASO is capable of solving various problems, due to low convergence speed and lack of proper balance between exploration and exploitation, it is not efficient enough in sorting out real-world convoluted problems. In the present paper, the convergence speed of ASO is improved using chaotic maps and Levy flight random walk. In addition, ASO is hybridized with the tree-seed algorithm (TSA) to improve exploration and exploitation capabilities and make a proper balance between them. TSA is an innovative intelligent meta-heuristic algorithm that has been inspired by the growth of trees and spreading their seeds and has a decent exploration ability. Furthermore, a novel technique has been applied on the proposed hybrid algorithm as a solution for departure of local optimums. Besides, the effectiveness of our contributions is validated by testing the proposed hybrid algorithm on a vast set of benchmark functions comprising unimodal, multimodal, fixed dimension, shifted–rotated and composite. The obtained results have been compared with several other new and powerful meta-heuristic algorithms in terms of descriptive and inferential statistics. In addition, the algorithms are tested on seven real-life engineering problems. The results of the experiments indicated the effectiveness of contributions and the superiority of the proposed hybrid algorithm over its akin counterparts. [ABSTRACT FROM AUTHOR]

Solving constrained engineering optimization problems is a highly significant issue, and many different approaches have been proposed in this regard. In this article, a modified farmland fertility algorithm (FFA) has been proposed. This algorithm improves new solutions by benefiting from neighborhoods produced by the new method. In the proposed algorithm, the FFA algorithm phases are modified in the update functions, and some of its variables are replaced with new ones. In the first phase of the algorithm, a mutation is also used to improve the solution of the problem with a particular rule. The results on CEC2019 standard functions were examined to determine the impact of the new parameters. Experiments were performed on 26 standard functions and two constrained engineering optimization problems. To make a better comparison, the analysis of variance, without parameters such as the Friedman test and Pairwise test, was used to evaluate and compare the algorithms. These experiments showed that the proposed algorithm is capable of high‐speed convergence and can minimize most standard functions and engineering constraints within the minimum time and the least function evaluation to the optimum value. The proposed FFA could outperform the previous version of the FFA with minimum modification in convergence, CPU‐time, and complexity. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a meerkat optimization algorithm(MOA) by simulating the behavior pattern of meerkats in nature. MOA is mainly inspired by the survival strategies of meerkat populations, whose sentinel mechanism controls meerkats to switch between different behavior patterns. Some mathematical properties of meerkat optimization algorithm are proved, and the advantages of MOA are verified with classical optimization test functions. MOA is applied to solve real-world engineering problems with constraints, which proves the effectiveness and superiority of MOA in solving such problems. • A new optimization algorithm is proposed based on the meerkat survival strategy. • The algorithm is called MOA and is used to solve constrained optimization problems. • The convergent property of MOA is proved strictly in theory. • It proves the effectiveness and superiority of MOA compared with other algorithms. [ABSTRACT FROM AUTHOR]

Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was the announced winner of the Congress on Evolutionary Computation (CEC) 2017 competition on numerical optimization, and is the state-of-the-art algorithm in the SHADE (Success-History based Adaptive Differential Evolution) algorithm series. However, the jSO algorithm converges prematurely in the search space with different dimensions and is prone to falling into local optimum during evolution, as well as the problem of decreasing population diversity. In this paper, a modified jSO algorithm (MjSO) is proposed which is based on cosine similarity with parameter adaptation and a novel opposition-based learning restart mechanism incorporated with symmetry to address the above problems, respectively. Moreover, it is well known that parameter setting has a significant impact on the performance of the algorithm and the search process can be divided into two symmetrical parts. Hence, a parameter control strategy based on a symmetric search process is introduced in the MjSO. The effectiveness of these designs is supported by presenting a population clustering analysis, along with a population diversity measure to evaluate the performance of the proposed algorithm, three state-of-the-art DE variant algorithms (EBLSHADE, ELSHADE-SPACMA, and SALSHADE-cnEPSin) and two original algorithms (jSO and LSHADE) are compared with it, for solving 30 CEC'17 benchmark functions and three classical engineering design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitions in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios. [ABSTRACT FROM AUTHOR]