The Dung Beetle Optimization (DBO) algorithm exhibits rapid convergence and robust search capabilities, yet its performance is constrained by excessive reliance on global best and worst solutions. To resolve these weaknesses, this paper introduces an enhanced DBO that incorporates multiple strategies, named DCWDBO. The dynamic opposition-based learning mechanism improves the quality of the initial population. Horizontal and vertical crossover strategies are incorporated to strengthen search capabilities. To preserve high population diversity throughout iterations, the original boundary-control mechanism is replaced with rules from the Wave Search Algorithm. To evaluate DCWDBO’s effectiveness, it was compared with PSO, SCA, SCSO, and standard DBO using benchmark functions from CEC 2017, 2020, and 2022. Results indicate that DCWDBO achieves reliable performance, demonstrating robust global exploration, stable convergence, and superior large-scale optimization capability.

One of the primary goals of optimization approaches is to strike a balance between exploitation and exploration strategies, thereby enhancing the efficiency of the search process. To improve this balance, considerable research efforts have been directed towards refining these strategies. This paper introduces a novel exploration approach for the Black Widow Optimization (BWO) algorithm, termed Improved BWO (IBWO), aimed at achieving a robust equilibrium between global and local search strategies. The proposed approach tracks and remembers the effective research areas during the research iteration and uses them to direct the subsequent research process toward the most promising areas of the search space. Consequently, this method facilitates convergence towards optimal global solutions, leading to the generation of higher-quality solutions. To evaluate its performance, IBWO is compared with five optimization techniques, including BWO, GA, PSO, ABC, and BBO, across 39 benchmark functions. Simulation results demonstrate that IBWO consistently maintains precision in performance, achieving superior fitness values in 87.2%, 74.4%, and 69.2% of total trials across three distinct simulation settings. These outcomes underscore the efficacy of IBWO in effectively leveraging prior search space information to enhance the balance between exploitation and exploration capabilities. The proposed IBWO has broad applicability, addressing real-world optimization challenges in pilgrim crowd management and transportation during Hajj, supply chain logistics, and energy distribution optimization.

Three simple metaphor-less optimization algorithms are developed in this paper for solving the unconstrained and constrained optimization problems. These algorithms are based on the best and worst solutions obtained during the optimization process and the random interactions between the candidate solutions. These algorithms require only the common control parameters like population size and number of iterations and do not require any algorithm-specific control parameters. The performance of the proposed algorithms is investigated by implementing these on 23 benchmark functions comprising 7 unimodal, 6 multimodal and 10 fixed-dimension multimodal functions. Additional computational experiments are conducted on 25 unconstrained and 2 constrained optimization problems. The proposed simple algorithms have shown good performance and are quite competitive. The research community may take advantage of these algorithms by adapting the same for solving different unconstrained and constrained optimization problems.