## 1 Introduction
MGAD is a novel adaptive evolutionary multitask optimization algorithm designed to address challenges in evolutionary multitask optimization, including dynamic control, inaccurate task selection, negative knowledge transfer, and decreased optimization performance due to increased uncertainty [25]. The TSLS-SCSO algorithm, an enhanced version of Sand Cat Swarm Optimization (SCSO), is proposed for autonomous path planning in dynamic environments. It incorporates a population initialization strategy, spiral search, Livy flight, tent chaotic mapping, and sparrow alert mechanism, achieving higher accuracy and competitiveness [26]. The method includes a PHFM-based domain transform strategy and the BHRN network structure. The input image is transformed into the PHFM domain for parsing and edge predictions. An encoder-decoder structure is used for mutual guidance learning across multiple stages. The shared encoder enhances feature propagation, while separate decoders predict parsing and edge results [27]. This study explores the use of chaotic systems in metaheuristic optimization techniques to improve search efficiency. The initial populations of the PSO and WSO algorithms are distributed according to Logistic, Chebyshev, Circle, sine, and Piecewise chaotic maps [28]. The paper proposes a Polynomial Chebyshev Symmetric Chaotic-based GJO (PCSCGJO) algorithm to improve image segmentation in image processing. The PCSCGJO algorithm combines a chaotic generating function with the Golden Jackal Optimizer (GJO)algorithm, aiming to avoid local optima and improve segmentation results. The simulation results show the PCSCGJO method's effectiveness in dealing with medical color images outperforms other metaheuristic algorithms in terms of quality and accuracy [29]. This paper proposes an improved Transit Search algorithm based on a chaotic map and local escape operator (CLTS). The algorithm introduces chaos initialization, simplifies transit criteria, improves convergence speed in the neighborhood phase, and balances exploration and exploitation. The experimental results show the CLTS algorithm achieves faster convergence speed and higher accuracy compared to the TS algorithm and other advanced algorithms. It also outperforms advanced image segmentation algorithms in terms of convergence and segmentation effects [30]. To improve metaheuristic algorithm performance, hybrid metaheuristic algorithms are proposed. It is common for metaheuristic algorithms to integrate two or more metaheuristic algorithms to solve optimization problems. Hybrid metaheuristic algorithms have the primary objective of utilizing the advantages of both algorithms while minimizing their drawbacks. Hybrid metaheuristic algorithms generally fine-tune exploration and exploitation [31]. A hybrid metaheuristic algorithm is proposed in order to achieve a balance between exploration and exploitation of metaheuristic algorithms. The performance of the convergence curve and the optimal solution during algorithm execution are essential for achieving the best result. The metaheuristic algorithm finds the optimal solution by analyzing the initial search space, whereas the algorithm structure is used to determine the optimal solution. The diversity of solutions is also important in this way. On the other hand, there is a relationship between the diversity of solutions and the speed of convergence. Fast convergence is often caused by low diversity. Most hybrid metaheuristic algorithms perform solution diversity, and the algorithm structure further promotes solution diversity [31; 32]. The most important issue to avoid is local trapping. This study makes the following major contributions:  
* A novel hybrid metaheuristic algorithm (COSGO) is proposed, which integrates grey wolf optimization (GWO) and Sand Cat Swarm Optimization (SCSO) to enhance global optimization performance. * The COSCO algorithm is specifically designed to overcome the local optima problem by improving exploration capabilities.