As science and technology improve, more and more complex global optimization difficulties arise in real-life situations. Finding the most perfect approximation and optimal solution using conventional numerical methods is intractable. Metaheuristic optimization approaches may be effective in achieving powerful global optimal solutions for these complex global optimization situations. Therefore, this paper proposes a new game-based algorithm called the running city game optimizer (RCGO), which mimics the game participant’s activity of playing the running city game. The RCGO is mathematically established by three newfangled search strategies: siege, defensive, and eliminated selection. The performance of the proposed RCGO algorithm in optimization is comprehensively evaluated on a set of 76 benchmark problems and 8 engineering optimization scenarios. Statistical and comparative results show that RCGO is more competitive with other state-of-the-art competing approaches in terms of solution quality and convergence efficiency, which stems from a proper balance between exploration and exploitation. Additionally, in the case of engineering optimization scenarios, the proposed RCGO is able to deliver superior fitting and occasionally competitive outcomes in optimization applications. Thus, the proposed RCGO is a viable optimization tool to easily and efficiently handle various optimization problems.

In order to improve the performance of the sparrow search algorithm (SSA), in this paper, a novel series of SSA variants is proposed by combining SSA with improved Tent chaos mutation (IT), Lévy flights mutation (LF), elite opposition-based learning mutation (EOBL), variable radius mutation (VR) and the combination of IT, LF, EOBL, and VR, namely, ITSSA, LFSSA, EOBLSSA, VRSSA, and CMSSA, respectively. Initially, the performance of these variants is evaluated on a comprehensive set of 31 benchmark test functions. Moreover, the performance of the best algorithm among these variants is compared with 19 state-of-the-art optimization algorithms to validate its performance on 31 benchmark test functions. The convergence and computational complexity of the best variant are also analyzed to test exploration, exploitation, and local optima avoidance. It is then employed on eight real-world constrained engineering problems to further verify its robustness. The experimental results reveal that the best algorithm of SSA variants outperforms other competitors and is highly effective in solving real-life cases.

Seagull optimization algorithm (SOA) has the disadvantages of low convergence accuracy, weak population diversity, and tendency to fall into local optimum, especially for high dimensional and multimodal problems. To overcome these shortcomings, initially, in this study, a shared SOA (SSOA) is proposed based on the combination of a sharing multi-leader strategy with a self-adaptive mutation operator. In addition, seven new variants of the SSOA algorithm are proposed employing the Gaussian mutation operator, Cauchy mutation operator, Lévy flights mutation operator, improved Tent chaos mutation operator, neighborhood centroid opposition-based learning mutation operator, elite opposition-based learning mutation operator, and simulated annealing algorithm combined with other mutation operators, namely, GSSOA, CSSOA, LFSSOA, ITSSOA, ESSOA, NSSOA, and CMSSOA, respectively. Then, the performance of these variants was evaluated on 23 benchmark functions, and the various performances of the best variant were evaluated on a comprehensive set of 43 benchmark problems and three real-world problems compared to other optimizers. Experimental and statistical results demonstrate that the proposed CMSSOA algorithm outperforms other variants of the SSOA algorithm and competitor approaches.

Specular reflection algorithm (SRA) was a single population meta-heuristic algorithm inspired by the physical function of mirror. However, similar to most of meta-heuristic algorithms, it had the disadvantages of weak population diversity, stagnation in local optimal and low convergence rate. In order to overcome these shortcomings, a chaotic multi-specular reflection optimization algorithm considering shared nodes (CMSRAS) was proposed by the combination of population strategy with shared node, improved Tent chaos strategy and Gaussian mutation strategy. Initially, a single population SRA was extended to the multi-population with shared node and the population was initialized by improved Tent chaos sequence to improve the quality of the initial solution and the population diversity, and to enhance the global search ability. Meanwhile, to strengthen the local search ability and the convergence accuracy, the Gaussian mutation and improved Tent chaotic disturbance strategy were introduced into SRA. And then the influence law and sensitivity analysis of the CMSRAS algorithm between the initial setting parameters were obtained based on the response surface method and the Sobol's method. Finally, compared with both 12 state-of-the-art algorithms and 8 well-known advanced algorithms, the performance of CMSRAS was evaluated on a comprehensive set of 32 benchmark problems. In addition, CMSRAS was applied to solve the complex optimization problems of engineering structure. The results demonstrated that proposed CMSRAS algorithm outperformed most competitive algorithms and efficiently solve the real-life global optimization problems.

The present study introduces MBSCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), with the aim of addressing inherent limitations in the original SCSO algorithm. In MBSCSO, the initial implementation of the enhanced Circle chaotic mapping combined with dynamic opposition-based learning (ECM-OBL) strategy, aims to enhance the global exploration capability of algorithms in the initial stage. Additionally, a nonlinear sensitivity factor (NSF) is adopted to improve the balance between exploration and exploitation. The adaptive alert strategy (AAS), Levy flight strategy (LFS), adaptive Sinh-Cosh spiral attack strategy (ASCAS) and dynamic random search technique (DRST) are designed to enhance the overall performance and efficiency of the algorithm. Finally, the superiority of the presented MBSCSO is validated through comprehensive evaluations on 64 benchmark functions and five typical engineering problems, which clearly demonstrates that the MBSCSO algorithm consistently outperforms or achieves comparable performance to other state-of-the-art optimization approaches.

The Snow Ablation Optimizer (SAO) is a powerful metaheuristic algorithm that emulates the process of snow sublimation and melting. Despite exhibiting competitive performance compared to classical algorithms in initial research, SAO still has certain limitations, including suboptimal convergence accuracy, inadequate population diversity, and premature convergence, particularly when faced with high-dimensional complex challenges. To address these limitations, this paper proposes a refined snow ablation optimizer featuring joint opposite selection and multi-strategy fusion (JMRSAO), which integrates three enhancement strategies. Firstly, a good point set opposition-based learning (GPSOL) approach is utilized to generate a population of high-quality individuals that are evenly distributed. This helps the algorithm converge quickly towards the appropriate search space. Next, in both exploration and exploitation phases, we introduce joint opposite selection (JOS) technology to effectively retain superior individuals for future iterations. This promotes information exchange among diverse individuals and establishes a strong balance between exploring new possibilities and exploiting existing knowledge. Furthermore, we incorporate the Powell mechanism (PM) to enhance local exploration and accelerate convergence towards improved solutions. The performance of JMRSAO is evaluated by comparing it with thirteen other metaheuristic algorithms on three benchmark experiments from IEEE CEC2005, IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 as well as six engineering problems. Experimental results demonstrate that JMRSAO effectively addresses the limitations of SAO and exhibits exceptional performance in solving various complex problems while showcasing its potential as an effective problem-solving tool.