Global optimization is widely adopted presently in a variety of practical and scientific problems. In this context, a group of widely used techniques are evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant-Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide series of test functions available from the relevant literature and compared against other evolutionary methods.

This paper presents an innovative optimization algorithm based on differential evolution that combines advanced mutation techniques with intelligent termination mechanisms. The proposed algorithm is designed to address the main limitations of classical differential evolution, offering improved performance for symmetric or non-symmetric optimization problems. The core scientific contribution of this research focuses on three key aspects. First, we develop a hybrid dual-strategy mutation system where the first strategy emphasizes exploration of the solution space through monitoring of the optimal solution, while the second strategy focuses on exploitation of promising regions using dynamically weighted differential terms. This dual mechanism ensures a balanced approach between discovering new solutions and improving existing ones. Second, the algorithm incorporates a novel majority dimension mechanism that evaluates candidate solutions through dimension-wise comparison with elite references (best sample and worst sample). This mechanism dynamically guides the search process by determining whether to intensify local exploitation or initiate global exploration based on majority voting across all the dimensions. Third, the work presents numerous new termination rules based on the quantitative evaluation of metric value homogeneity. These rules extend beyond traditional convergence checks by incorporating multidimensional criteria that consider both the solution distribution and evolutionary dynamics. This system enables more sophisticated and adaptive decision-making regarding the optimal stopping point of the optimization process. The methodology is validated through extensive experimental procedures covering a wide range of optimization problems. The results demonstrate significant improvements in both solution quality and computational efficiency, particularly for high-dimensional problems with numerous local optima. The research findings highlight the proposed algorithm’s potential as a high-performance tool for solving complex optimization challenges in contemporary scientific and technological contexts.

In the dynamically evolving field of collective computational optimization, modern approaches increasingly incorporate bio-inspired techniques, such as Smell Agent Optimization (SAO), to address complex, high-dimensional problems inherent to contemporary scientific and industrial applications. While these methods are distinguished by their dynamic convergence and heuristic ability to explore vast solution spaces, their growing computational complexity hinders their application in real-world, large-scale scenarios where simultaneous speed and precision are critical. To overcome this challenge, the present research advances a pioneering parallel implementation of SAO, which transcends simple workload distribution by integrating dynamic collaboration mechanisms and intelligent information dispersal among autonomous subpopulations. Concurrently, the method is enriched with innovative rules for exchanging optimal solutions between subpopulations. These rules not only prevent premature convergence to local minima but also establish a continuous flow of information that accelerates the global exploration of the solution space. Experimental validation of the proposed method demonstrated that, through optimized parameterization of the diffusion mechanisms, SAO’s efficiency can exceed 50%, achieving simultaneous reductions in both the number of objective function evaluations and total execution time. This outcome holds particular significance in high-dimensional problems, where balancing computational cost and accuracy is a decisive factor. These findings not only underscore the potential of parallel SAO to deliver sustainable solutions to real-world challenges but also open new horizons in the theory and practice of collective optimization. The implications extend to domains such as large-scale data analysis, autonomous systems, and adaptive resource management, where rapid and precise optimization is paramount.

Global optimization is used in many practical and scientific problems. For this reason, various computational techniques have been developed. Particularly important are the evolutionary techniques, which simulate natural phenomena with the aim of detecting the global minimum in complex problems. A new evolutionary method is the Eel and Grouper Optimization (EGO) algorithm, inspired by the symbiotic relationship and foraging strategy of eels and groupers in marine ecosystems. In the present work, a series of improvements are proposed that aim both at the efficiency of the algorithm to discover the total minimum of multidimensional functions and at the reduction in the required execution time through the effective reduction in the number of functional evaluations. These modifications include the incorporation of a stochastic termination technique as well as an improvement sampling technique. The proposed modifications are tested on multidimensional functions available from the relevant literature and compared with other evolutionary methods.

Global optimization represents a fundamental challenge in computer science and engineering, as it aims to identify high-quality solutions to problems spanning from moderate to extremely high dimensionality. The Differential Evolution (DE) algorithm is a population-based algorithm like Genetic Algorithms (GAs) and uses similar operators such as crossover, mutation and selection. The proposed method introduces a set of methodological enhancements designed to increase both the robustness and the computational efficiency of the classical DE framework. Specifically, an adaptive termination criterion is incorporated, enabling early stopping based on statistical measures of convergence and population stagnation. Furthermore, a population sampling strategy based on k-means clustering is employed to enhance exploration and improve the redistribution of individuals in high-dimensional search spaces. This mechanism enables structured population renewal and effectively mitigates premature convergence. The enhanced algorithm was evaluated on standard large-scale numerical optimization benchmarks and compared with established global optimization methods. The experimental results indicate substantial improvements in convergence speed, scalability and solution stability.

In recent years, large-scale optimization problems have become increasingly common in various fields, such as machine learning and data analysis, generating increased references to both computational cost and accurate solutions. The Grey Wolf Optimizer (GWO) is an efficient collective intelligence algorithm; however, its performance may be limited when high-permissive problems are allowed or in environments with strong multimodal landscapes. In this paper, we propose the Parallel Balanced Grey Wolf Optimizer (ParallelBGWO), a parallel extension of GWO that aims to improve the balance between exploration and exploitation of the search space. The proposed algorithm divides the total population into multiple subpopulations, which cooperate through information exchange. This cooperation reduces the probability of premature convergence and enhances the global search. The parallel implementation exploits modern multi-core computing architectures, achieving a significant reduction in execution time, while at the same time maintaining or improving the quality of the final solutions. Experimental evaluations on high-dimensional benchmark functions show that ParallelBGWO exhibits faster convergence and a reduced number of objective function evaluations compared to the classical version of GWO and other prominent methods. The results highlight ParallelBGWO as an efficient approach for demanding global optimization problems.