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In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

The Honey Badger Algorithm (HBA) represents a novel swarm intelligence optimization algorithm introduced in recent years. However, its predominant constraints are linked to inadequate convergence accuracy and a vulnerability to entrapment in local optima. In an effort to mitigate these challenges, this paper introduces an Improved Honey Badger Algorithm Based on a Hybrid Strategy (OHBA). Firstly, during the population initialization phase, a method involving the utilization of a good point set is introduced to enhance the diversity and introduce more randomness into the population. Secondly, in the position update phase, the Beta distribution is employed as an alternative to the Uniform distribution, aiming to strike a balance between global exploration and local exploitation capabilities. Thirdly, an improved adaptive density factor strategy is incorporated into both global and local position updates to enhance the algorithm's convergence precision and speed. Lastly, within the global exploration stage, a Cauchy mutation strategy based on the Sine chaotic mapping is introduced to facilitate the algorithm in overcoming local optima and reinforcing its optimization capabilities. The improved algorithm's performance has been evaluated through a comprehensive set of assessments, including CEC-2017 functions, CEC-2022 functions, Wilcoxon rank-sum tests, and practical engineering optimization problems. These evaluations were undertaken to assess the algorithm in comparison to classical intelligent optimization algorithms. The experimental results show that OHBA possesses significant advantages in terms of convergence speed, optimization accuracy, robustness and its practical utility and effectiveness in addressing complex optimization challenges. This establishes OHBA as a highly competitive option in these critical aspects of optimization. [ABSTRACT FROM AUTHOR]