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By combining opposition-based learning techniques with conventional African Vulture Optimization (AVO), this study offers a notable improvement in the handling of optimization problems. Despite the limitations of AVO, such as issues involving extremely rough search spaces, more iterations or function evaluations are necessary. To overcome this limitation, our proposed paper, an enhanced opposition-based learning (EOBL), speeds up the convergence and, at the same time, assists the algorithm in escaping local optima. A combination of this new technique with AVO, the Enhanced Opposition-based African Vulture Optimizer (EOBAVO), is proposed. The performance of the suggested EOBAVO was evaluated through experiments using the CEC2005 and CEC2022 benchmark functions in addition to seven engineering challenges. Furthermore, statistical analyses, including the t-test and Wilcoxon rank-sum test, were conducted, and they demonstrated that the proposed EOBAVO surpasses several of the leading algorithms currently in use. The results indicate that the proposed approach can be regarded as a competent and efficient solution for complex optimization challenges.

This study is an attempt to improve the recently introduced American Zebra Optimization Algorithm (AZOA), which is inspired by the leadership dynamics and scavenging behaviour of American zebras in nature. Although AZOA demonstrates strong exploration capability, it suffers from certain limitations, such as weak exploitation ability and a tendency to become trapped in local optima when dealing with complex optimization problems. To alleviate these challenges, a novel strategy called Enhanced Opposition-Based Learning (EOBL) is suggested and integrated with the AZOA framework. The EOBL mechanism extends the traditional opposition-based learning by incorporating a degree of controlled randomness, aiming to achieve a better balance between exploration and exploitation during the search process. Consequently, an improved algorithm termed the Enhanced Opposition-Based American Zebra Optimization Algorithm (EOBAZOA) is proposed to enhance the performance of the standard AZOA. The effectiveness of EOBAZOA has been validated through extensive experimentation on both classical benchmark functions from CEC2005 and recent test suites from CEC2022, in addition to a set of real-world engineering design problems. Furthermore, rigorous statistical analysis, such as the t-test has been conducted to assess the robustness and reliability of the results. The experimental findings confirm that the proposed EOBAZOA approach achieves superior performance than other cutting-edge optimization algorithms in both benchmark and real-world engineering problem scenarios.

This paper presents a new metaheuristic algorithm by enhancing one of the recently proposed optimizers named Aquila optimizer (AO). The enhanced AO (enAO) algorithm is constructed by employing a novel modified opposition-based learning (OBL) mechanism and Nelder-Mead (NM) simplex search method. The novel modified OBL aids the AO in further diversification while the NM method increases the intensification. The enAO algorithm is first demonstrated to have more extraordinary ability than the original AO algorithm by employing challenging benchmark functions from the CEC 2019 test suite. The constructed enAO algorithm is proposed to design a PID plus second-order derivative (PIDD2) controller used in an automatic voltage regulator (AVR) system. To reach better efficiency, a novel objective function is also proposed in this paper. Initially, the proposed enAO-PIDD2 approach is demonstrated to be superior in terms of transient and frequency responses along with robustness and disturbance rejection compared to other available and best performing PID, fractional order PID (FOPID), PID acceleration (PIDA), and PIDD2 controllers tuned with different practical algorithms. Moreover, the superior performance of the proposed approach is also demonstrated comparatively using other available techniques for the AVR system reported in the last six years.

When solving practical optimization problems by metaheuristic algorithms (MAs), an important issue is how to balance exploration and exploitation. This article develops a novel hybrid grasshopper optimization algorithm (HGOA) to solve the above issue. In HGOA, an enhanced grasshopper optimization algorithm with the nonlinear control parameter and a modified butterfly optimization algorithm with the dynamic inertia weight are hybridized based on the probabilistic selection mechanism. Then the centroid opposition-based learning method is utilized to select the best solution between the original individual and its opposite solution. Furthermore, we introduce a self-adaptive pattern search technique as a local search engine to reinforce the exploitation capacity of the algorithm. The comparison of the proposed method on several benchmark problems and five engineering optimization issues with selected state-of-the-art techniques demonstrates that the developed method performs competitively and effectively. The source code of HGOA is publicly available at https://github.com/denglingyun123/A-novel-hybrid-grasshopper-optimization-algorithm .

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study.

An enhanced Fata Morgana optimizer, called eFATA is presented to improve convergence speed and robustness on complex landscapes. We target FATA because, while effective, it can lose diversity and stagnate on multimodal problems; eFATA adds Opposition-Based Learning (diversified initialization) and a Local Escaping Operator (adaptive local exploration) to rebalance exploration and exploitation. On the CEC’22 benchmark suite, eFATA ranked first overall (Friedman mean rank ), achieved the best mean fitness on 11/12 functions, and reached the known global optimum on 11/12 benchmarks, with a success rate of 91.7%. Beyond synthetic tests, we apply eFATA to tune Support Vector Regression (SVR) for maximum temperature forecasting across nine Egyptian governorates (Agricultural Research Center data), yielding consistently lower RMSE and more stable convergence than competing optimizers. These results show that eFATA advances metaheuristic optimization and provides a reliable, accurate framework for practical climate-change forecasting.

The Pathfinder Algorithm (PFA) is a bionic swarm intelligence optimization algorithm inspired by simulating the cooperative movement of animal groups in nature to search for prey. Based on fitness, the algorithm classifies search individuals into leaders and followers. However, PFA fails to effectively balance the optimization capabilities of leaders and followers, leading to problems such as insufficient population diversity and slow convergence speed in the original algorithm. To address these issues, this paper proposes an enhanced pathfinder algorithm based on multi-strategy (EODE-PFA). Through the synergistic effects of multiple improved strategies, it effectively solves the balance problem between global exploration and local optimization of the algorithm. To verify the performance of EODE-PFA, this paper applies it to CEC2022 benchmark functions, three types of complex engineering optimization problems, and six sets of feature selection problems, respectively, and compares it with eight mature optimization algorithms. Experimental results show that in three different scenarios, EODE-PFA has significant advantages and competitiveness in both convergence speed and solution accuracy, fully verifying its engineering practicality and scenario universality. To highlight the synergistic effects and overall gains of multiple improved strategies, ablation experiments are conducted on key strategies. To further verify the statistical significance of the experimental results, the Wilcoxon signed-rank test is performed in this study. In addition, for feature selection problems, this study selects UCI real datasets with different real-world scenarios and dimensions, and the results show that the algorithm can still effectively balance exploration and exploitation capabilities in discrete scenarios.

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure.