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The Kepler optimization algorithm (KOA) is a metaheuristic algorithm based on Kepler's laws of planetary motion and has demonstrated outstanding performance in multiple test sets and for various optimization issues. However, the KOA is hampered by the limitations of insufficient convergence accuracy, weak global search ability, and slow convergence speed. To address these deficiencies, this paper presents a multi-strategy fusion Kepler optimization algorithm (MKOA). Firstly, the algorithm initializes the population using Good Point Set, enhancing population diversity. Secondly, Dynamic Opposition-Based Learning is applied for population individuals to further improve its global exploration effectiveness. Furthermore, we introduce the Normal Cloud Model to perturb the best solution, improving its convergence rate and accuracy. Finally, a new position-update strategy is introduced to balance local and global search, helping KOA escape local optima. To test the performance of the MKOA, we uses the CEC2017 and CEC2019 test suites for testing. The data indicate that the MKOA has more advantages than other algorithms in terms of practicality and effectiveness. Aiming at the engineering issue, this study selected three classic engineering cases. The results reveal that the MKOA demonstrates strong applicability in engineering practice. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic optimization algorithms offers a promising approach for enhancing the search performance and achieving optimal solutions. The main goal of hybridization is to combine algorithms in a manner that eliminates their disadvantages while enhancing their capabilities. The Cheetah Optimizer (CO), a bioinspired algorithm, effectively combines various strategies to navigate the search space. Despite its success in diverse engineering applications, CO faces challenges, such as slow convergence and stagnation in local optima. This study introduces a novel hybridization of the Cheetah Optimizer, incorporating Opposition-Based Learning (OBL) and diversity measures to enhance its exploratory capabilities. The OBL mechanism updates solutions by considering a counterpart in the opposite region of the search space, thereby expanding the solution range. In addition, diversity measures ensure a balance between exploration and exploitation throughout the evolutionary process. The combination of these methodologies enables the developed algorithm to achieve faster convergence rates, while simultaneously diminishing the likelihood of becoming trapped in local optimum solutions. To validate the performance of the proposed algorithm, it was compared with other optimization methods by using a representative set of functions with different degrees of complexity. The results demonstrated its ability to produce competitive results in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

In this paper, aiming at the problems of large randomness, low convergence accuracy, and easy falling into local optimum in the application of sparrow search algorithm to UAV three-dimensional path planning, a dynamic step opposition-based learning sparrow search algorithm is proposed. The algorithm first uses a good point set in the population initialization phase to improve the quality of the initial solution; secondly, the piecewise dynamic step size is used to optimize the update formula of the discoverer, and the extensive search is carried out in the early stage of the iteration. In the later stage, the known area is mined as much as possible to improve the search accuracy and convergence speed of the algorithm. Then, the crazy operator is integrated to optimize the predator update formula and improve the local search ability. Finally, t-distribution opposition-based learning is used to prevent the algorithm from falling into the local optimum. In this paper, the effectiveness of the improved algorithm is verified by six test functions and applied to the three-dimensional path planning of UAVs. The experimental results show that the proposed algorithm has a faster convergence speed than the traditional algorithm, and the planned path is shorter. [ABSTRACT FROM AUTHOR]

The study introduces a novel approach for skin lesion classification that leverages bilinear interpolation-based feature extraction and an enhanced Remora Optimization Algorithm (ROA). This research is motivated by the high prevalence of skin cancer globally and the necessity for early detection, particularly in underserved areas where access to dermatological expertise is limited. Traditional dermoscopic datasets, typically captured in controlled clinical environments, do not adequately represent the variability of real-world smartphone images, which often suffer from issues like varied lighting and motion blur. The proposed method refines visual details by integrating multi-scale spatial information, significantly enhancing classification accuracy. The enhanced ROA optimizes parameter selection, addressing challenges of high-dimensional data and hyper-parameter tuning in deep learning models. This innovative combination surpasses existing classification methods, offering a promising early screening tool, especially beneficial in rural areas. The results demonstrate the proposed architecture’s effectiveness, achieving accuracies of 83.71%, 97.21%, and 95.6% on the PAD-UFES-20, ISIC 2016, and ISIC 2017 datasets, respectively, highlighting the potential of advanced computational techniques in augmenting the diagnostic process for skin cancer. [ABSTRACT FROM AUTHOR]

A mobile ad hoc network (MANET) comprises multiple autonomous unmanned aerial vehicles (UAVs) connected in an ad hoc manner. MANETs are key components in achieving different services in smart cities. One of the main issues in the MANET is UAV placement, which refers to finding the optimal positions of UAVs. Recently, researchers proposed several machine-learning methods for UAV placement. The existing techniques obtained promising results; however, their performance is far from the best, and more effort is needed. This paper introduces an adaptive local search-based arithmetic optimization (LSAO) algorithm for UAV placement. The incentive mechanism of LSAO is enhancing the search dynamics by embedding an adaptive switching probability, a chaotic local search, and an opposition-based learning strategy into the standard AO algorithm. The proposed method is benchmarked on well-known placement test cases, and the results are verified by a comparative study with state-of-the-art algorithms. The results confirm that LSAO generated competitive outcomes compared to its peers in most simulation benchmarks. The LSAO obtained the first rank in terms of coverage, connectivity, and total fitness values among comparison algorithms. [ABSTRACT FROM AUTHOR]

Wild horse optimizer (WHO), inspired by the social life behavior of wild horses, is highly competitive in solving complex optimization problems. However, the original WHO is slow to converge in the later iterations, has low search accuracy, and is prone to fall into local optimum. In order to solve these problems, an improved wild horse optimization algorithm based on reinforcement learning and hybrid multi-strategy (IWHO) is proposed in this paper. Firstly, the initialization method of opposition-based learning is used to increase the population diversity and improve the quality of the initialized population. Secondly, the Q-Learning mechanism in reinforcement learning is introduced to establish a switching mechanism between grazing and mating behaviors of individual foals to guide the behavioral choices. Thirdly, a defense strategy is utilized to improve the algorithm’s optimization accuracy. Finally, last place elimination mechanism is adopted to eliminate the worst individual in each group and replaced it by random initialization to avoid the algorithm falling into a local optimum. The IWHO algorithm is tested on the CEC 2022 benchmark test suite and three practical engineering problems. The results show that IWHO has better performance than other algorithms. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) is a swarm intelligent algorithm that mimics the collective hunting strategy of Harris hawks. Although it has specific advantages over other algorithms in local exploitation for feasible solutions, the original HHO may perform poorly in balancing locally meticulous exploitation with globally exploratory search. This imbalanced behavior leads to a global impact, which may result in slow convergence, inaccuracy, or insufficient search coverage, and quickly fall into local optima. To this end, an improved opposition-based learning Harris hawks optimization with steepest convergence (OHHOS) is proposed to solve the optimization problems of continuous function and engineering problems. The opposition-based learning is very helpful in improving the quality of initial population as well as jumping out of local optima in the later iteration process, while the steepest convergence technique performs well in accelerating the convergence process and delving deeper into potential solutions. At the same time, the nonlinear energy factor is introduced to better balance the local and global search capabilities of the algorithm. Finally, the algorithm is compared with other heuristic algorithms on 29 CEC2017 benchmark functions and three typical engineering problems to verify the significant performance of the proposed method. The experimental results indicate that the newly proposed algorithm exhibits excellent performance in competition with the HHO as well as other recognized optimizers. [ABSTRACT FROM AUTHOR]

The rapid rise in volume and feature dimensions is negatively impacting machine learning and many other areas, leading to worse classification accuracy and higher computational costs. Feature Selection (FS) methods are crucial to lessen feature dimensionality, which act by removing attributes like irrelevant and less informative information which may have a detrimental impact on the performance of classifiers. This paper presents an Enhanced variant of the Coati Optimization Algorithm (ECOA) that features a better search ability than the basic COA. The COA algorithm was newly evolved to imitate the behavior of coatis when they hunt and attack iguanas as well as when they try to flee from predators. Although the authors of this algorithm state that it is promising, it occasionally exhibits poor search performance and early convergence. To mitigate these issues, the ECOA algorithm was proposed that makes use of elite opposite-based learning in addition to some adaptive search mechanisms. ECOA is expected to have an improved search mechanism and can prevent trapping at local optimum, depending on the mutation, mutation neighborhood search, and rollback procedures. Moreover, it enhances population variety and convergence rate. The COA and ECOA algorithms were used to solve FS problems by selecting optimal feature subsets based on a binary version of each adopted algorithm and the k-Nearest Neighbor (k-NN) classifier. To assess the performance of the Binary ECOA (BECOA), a number of experiments was performed on 24 datasets collected from many sources. Further, six criteria-sensitivity, specificity, classification accuracy, fitness value, number of chosen features, and run time-were used to assess the performance of BECOA. Experimental findings show the excellence of BECOA over other k-NN based FS methods, including Binary COA (BCOA) and other binary optimization methods, in a number of assessment aspects. In particular, among the 24 datasets deemed, BECOA, which yielded the best overall results among all other competing binary algorithms, was able to exclusively outperform the others in 7 datasets in terms of classification accuracy, 11 datasets in terms of specificity, 5 datasets in terms of sensitivity, 10 datasets in terms of number of selected features, 4 in terms of run-time, and 14 datasets in terms of fitness values. [ABSTRACT FROM AUTHOR]

Rapid advances in computer science and material science have made intelligent algorithms promising for solving material design problems. Particle swarm optimization (PSO) algorithm, as a typical swarm intelligence algorithm, is utilized in this paper as the tool to solve the problem of material stiffness optimization for homogenizing the contact stress distribution. In order to achieve an effective and efficient solution, the opposition-based learning (OBL) technique is introduced and combined with PSO to increases its exploration ability. Two mutation strategies, namely, dimension-by-dimension opposition-based learning and multi-dimensional random opposition-based learning mutation strategies, are further proposed to increase the population diversity of PSO. On this basis, algorithms named DEOBL-PSO and MREOBL-PSO are developed. The developed DEOBL-PSO and MREOBL-PSO are then applied to the field of nonlinear contact in engineering, and the specific problem of material stiffness optimization for homogenizing the contact stress distribution is solved. Impressive results are obtained. The contact stress distributing uniformity is substantially improved by material stiffness optimization using DEOBL-PSO and MREOBL-PSO. Additionally, new relation between material stiffness distribution and contact stress distribution is observed, and the effect of the material stiffness variation range on the contact stress distribution is further explored. [ABSTRACT FROM AUTHOR]

In response to the issues of local optima entrapment, slow convergence, and low optimization accuracy in Butterfly optimization algorithm (BOA), this paper proposes a hybrid Butterfly and Newton–Raphson swarm intelligence algorithm based on Opposition-based learning (BOANRBO). Firstly, by Opposition-based learning, the initialization strategy of the butterfly algorithm is improved to accelerate convergence. Secondly, adaptive perception modal factors are introduced into the original butterfly algorithm, controlling the adjustment rate through the adjustment factor α to enhance the algorithm's global search capability. Then, the exploration probability p is dynamically adjusted based on the algorithm's runtime, increasing or decreasing exploration probability by examining changes in fitness to achieve a balance between exploration and exploitation. Finally, the exploration capability of BOA is enhanced by incorporating the Newton–Raphson-based optimizer (NRBO) to help BOA avoid local optima traps. The optimization performance of BOANRBO is evaluated on 65 standard benchmark functions from CEC-2005, CEC-2017, and CEC-2022, and the obtained optimization results are compared with the performance of 17 other well-known algorithms. Simulation results indicate that in the 12 test functions of CEC-2022, the BOANRBO algorithm achieved 8 optimal results (66.7%). In CEC-2017, out of 30 test functions, it obtained 27 optimal results (90%). In CEC-2005, among 23 test functions, it secured 22 optimal results (95.6%). Additionally, experiments have validated the algorithm's practicality and superior performance in 5 engineering design optimization problems and 2 real-world problems. [ABSTRACT FROM AUTHOR]

The machine learning process in high-dimensional datasets is far more complicated than in low-dimensional datasets. In high-dimensional datasets, Feature Selection (FS) is necessary to decrease the complexity of learning. However, FS in high-dimensional datasets is a complex process that requires the combination of several search techniques. The Chimp Optimization Algorithm, known as ChOA, is a new meta-heuristic method inspired by the chimps' individual intellect and sexual incentive in cooperative hunting. It is basically employed in solving complex continuous optimization problems, while its binary version is frequently utilized in solving difficult binary optimization problems. Both versions of ChOA are subject to premature convergence and are incapable of effectively solving high-dimensional optimization problems. This paper proposes the Binary Improved ChOA Algorithm (BICHOA) for solving the bi-objective, high-dimensional FS problems (i.e., high-dimensional FS problems that aim to maximize the classifier's accuracy and minimize the number of selected features from a dataset). BICHOA improves the performance of ChOA using four new exploration and exploitation techniques. First, it employs the opposition-based learning approach to initially create a population of diverse binary feasible solutions. Second, it incorporates the Lévy mutation function in the main probabilistic update function of ChOA to boost its searching and exploring capabilities. Third, it uses an iterative exploration technique based on an exploratory local search method called the β -hill climbing algorithm. Finally, it employs a new binary time-varying transfer function to calculate binary feasible solutions from the continuous feasible solutions generated by the update equations of the ChOA and β -hill climbing algorithms. BICHOA's performance was assessed and compared against six machine learning classifiers, five integer programming methods, and nine efficient popular optimization algorithms using 25 real-world high-dimensional datasets from various domains. According to the overall experimental findings, BICHOA scored the highest accuracy, best objective value, and fewest selected features for each of the 25 real-world high-dimensional datasets. Besides, the reliability of the experimental findings was established using Friedman and Wilcoxon statistical tests. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is an effective classification tool and maturely used in various fields. However, its performance is very sensitive to parameters. As a newly proposed swarm intelligence algorithm, snake optimizer algorithm (SO) can help to solve the parameter selection problem. Nevertheless, SO has the shortcomings of weak population initialization, slow convergence speed in the early stage, and being easy to fall into local optimization. To address these problems, an improved snake optimizer algorithm (ISO) was proposed. The mirror opposition-based learning mechanism (MOBL) improved the population quality to enhance the optimization speed. The novel evolutionary population dynamics model (NEPD) was beneficial for searching accurately. The differential evolution strategy (DES) helped to reduce the probability of falling into local optimal value. The experimental results of classical benchmark functions and CEC2022 showed that ISO had higher optimization precision and faster convergence rate. In addition, it was also applied to the parameter selection of SVM to demonstrate the effectiveness of the proposed ISO.

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Artificial rabbits optimization (ARO) is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature. However, for solving optimization problems, the ARO algorithm shows slow convergence speed and can fall into local minima. To overcome these drawbacks, this paper proposes chaotic opposition-based learning ARO (COARO), an improved version of the ARO algorithm that incorporates opposition-based learning (OBL) and chaotic local search (CLS) techniques. By adding OBL to ARO, the convergence speed of the algorithm increases and it explores the search space better. Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently, since their ergodicity and non-repetitive properties. The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions. The outcomes have been compared with the most recent optimization algorithms. Additionally, the COARO algorithm's problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms. This study also introduces a binary variant of the continuous COARO algorithm, named BCOARO. The performance of BCOARO was evaluated on the breast cancer dataset. The effectiveness of BCOARO has been compared with different feature selection algorithms. The proposed BCOARO outperforms alternative algorithms, according to the findings obtained for real applications in terms of accuracy performance, and fitness value. Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Coordinate measuring machines (CMMs) are utilized to acquire coordinate data from manufactured surfaces for inspection reasons. These data are employed to gauge the geometric form errors associated with the surface. An optimization procedure of fitting a substitute surface to the measured points is applied to assess the form error. Since the traditional least-squares approach is susceptible to overestimation, it leads to unreasonable rejections. This paper implements a modified differential evolution (DE) algorithm to estimate the minimum zone femoral head sphericity. In this algorithm, opposition-based learning is considered for population initialization, and an adaptive scheme is enacted for scaling factor and crossover probability. The coefficients of the correlation factor and the uncertainty propagation are also measured so that the result's uncertainty can be determined. Undoubtedly, the credibility and plausibility of inspection outcomes are strengthened by evaluating measurement uncertainty. Several data sets are used to corroborate the outcome of the DE algorithm. CMM validation shows that the modified DE algorithm can measure sphericity with high precision and consistency. This algorithm allows for an adequate initial solution and adaptability to address a wide range of industrial problems. It ensures a proper balance between exploitation and exploration capabilities. Thus, the suggested methodology, based on the computational results, is feasible for the online deployment of the sphericity evaluation. The adopted DE strategy is simple to use, has few controlling variables, and is computationally less expensive. It guarantees a robust solution and can be used to compute different form errors. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow convergence, low accuracy, and easy to fall into local optimum of the slime mould algorithm (SMA), we propose an improved SMA (OJESMA). OJESMA improves the performance of the algorithm by combining strategies based on opposition-based learning, joint opposite selection, and equilibrium optimizer. First, we introduce an adversarial learning-opposition-based learning, in generating the initial population of slime molds. Second, we incorporate a joint inverse selection strategy, including selective leading opposition and dynamic opposite. Finally, we introduce the balanced candidate principle of the equilibrium optimizer algorithm into SMA, which enhances the algorithm's optimal search capability and anti-stagnation ability. We conducted optimization search experiments on 29 test functions from CEC2017 and 10 benchmark test functions from CEC2020, as well as nonparametric statistical analysis (Friedman and Wilcoxon). The experimental results and non-parametric test results show that OJESMA has better optimization accuracy, convergence performance, and stability. To further validate the effectiveness of the algorithm, we also performed optimization tests on six engineering problems and the variable index Muskingum. In summary, OJESMA demonstrates its practical value and advantages in solving various complex optimization problems with its excellent performance, providing new perspectives and methods for the development of optimization algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Most of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the "hybrid opposition learning-based Aquila Optimizer" (HOLAO) for optimizing TCQET decisions in generalized construction projects. Design/methodology/approach: In this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution. Findings: The efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator. Originality/value: This paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

An efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC'20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration. [ABSTRACT FROM AUTHOR]

This study introduces a novel technique for optimizing structural designs, focusing on creating lightweight structures that meet specific constraints. The main objective is to develop a new model for mass optimization in structural trusses using the opposition-based geometric mean optimizer (oGMO). This model combines the geometric mean optimizer (GMO) with the opposition-based learning (OBL) mechanism, enabling effective optimization of truss layouts utilizing both discrete and continuous variables. The efficacy of the oGMO model is assessed through various scenarios, including 25-bar and 72-bar spatial truss structures. These comprehensive assessments illustrate the effectiveness of the model, indicating that oGMO consistently produces superior designs compared to existing methods. Moreover, oGMO demonstrates remarkable computational efficiency, positioning it as a promising tool for structural design optimization, especially for achieving lightweight truss configurations. [ABSTRACT FROM AUTHOR]

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The Chimp Optimization Algorithm (ChOA) has emerged as a highly efficient optimization technique, demonstrating its prowess across diverse problem domains. However, its reliance on local search methods presents vulnerabilities, such as diminished diversity, susceptibility to premature convergence, and local minima. Thus, this study proposes two versions of enhancement the basic version of ChOA. The first version called ChOAO, integrates Opposition-based learning (OBL) to foster superior solution selection. The second version called ChOAORL, utilizes the concept of Reinforcement Learning (RL) to enhance the local search capabilities of ChOAO. It also effectively mitigates the risk of trapping the algorithm in local optima. The proposed versions are assessed utilizing the Friedman rank test on two sets of benchmark functions CEC 2017 and real-world problems CEC 2011. The results illustrate that ChOAORL achieved the best rank using CEC 2017 in both dimensions, 10 with a 1.48 mean rank and 30 with a 1.42 mean rank. Also, it outperformed other similar algorithms in terms of convergence precision and stability in all CEC 2011 real problems. [ABSTRACT FROM AUTHOR]

Mathematical models of Barnacle Mating Optimization (BMO) are based on observations of real-world barnacle mating behaviors such as sperm casting and self-fertilization. Nevertheless, BMO considers penis length to produce new offspring through pseudo-copulated mating behavior, with no constraints like strong wave motion, food availability, or wind direction considered. Exploration and exploitation are two crucial optimization stages as we implement the constrained BMO. They are informed by models of navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement experienced by barnacles during mating. We will later integrate opposition-based learning (OBL) with constrained BMO (C-BMO) to improve its exploratory behavior while retaining a quick convergence rate. Rather than opposing all barnacle dimensions, we just opposed those that went over the border. In addition to increasing efficiency by cutting down on wasted time spent exploring, this also increases the likelihood of stumbling onto optimal solutions. After that, it is put through its paces in a real-world case study, where it proves to be superior to the most cutting-edge algorithms available.

Abstract In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%.

In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%. [ABSTRACT FROM AUTHOR]

In this article, an improved harmony search algorithm (IHSA) that utilizes opposition-based learning is presented for solving the maximal covering location problem (MCLP). The MCLP is a well-known facility location problem where a fixed number of facilities are opened at a given potential set of facility locations such that the sum of the demands of customers covered by the open facilities is maximized. Here, the performance of the harmony search algorithm (HSA) is improved by incorporating opposition-based learning that utilizes opposite, quasi-opposite and quasi-reflected numbers. Moreover, a local search heuristic is used to improve the performance of the HSA further. The proposed IHSA is employed to solve 83 real-world MCLP instances. The performance of the IHSA is compared with a Lagrangean/surrogate relaxation-based heuristic, a customized genetic algorithm with local refinement, and an improved chemical reaction optimization-based algorithm. The proposed IHSA is found to perform well in solving the MCLP instances. [ABSTRACT FROM AUTHOR]

The Runge-Kutta Optimization (RUNGE) algorithm is a recently proposed metaphor-free metaheuristic optimizer borrowing practical mathematical foundations of the famous Runge-Kutta differential equation solver. Despite its relatively new emergence, this algorithm has several applications in various branches of scientific fields. However, there is still much room for improvement as it suffers from premature convergence resulting from inefficient search space exploration. To overcome this algorithmic drawback, this research study proposes a brand-new quasi-dynamic opposition-based learning (QDOPP) mechanism to be implemented in a standard Runge-Kutta optimizer to eliminate the local minimum points over the search space. Enhancing the asymmetric search hyperspace by taking advantage of various positions of the current solution within the domain is the critical novelty to enrich general diversity in the population, significantly improving the algorithm's overall exploration capability. To validate the effectivity of the proposed RUNGE-QDOPP method, thirty-four multidimensional optimization benchmark problems comprised of unimodal and multimodal test functions with various dimensionalities have been solved, and the corresponding results are compared against the predictions obtained from the other opposition-based learning variants as well as some state-of-art literature optimizers. Furthermore, six constrained engineering design problems with different functional characteristics have been solved, and the respective results are benchmarked against those obtained for the well-known optimizers. Comparison of the solution outcomes with literature optimizers for constrained and unconstrained test problems reveals that the proposed QDOPP has significant advantages over its counterparts regarding solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Crayfish optimization algorithm (COA) is a novel bionic metaheuristic algorithm with high convergence speed and solution accuracy. However, in some complex optimization problems and real application scenarios, the performance of COA is not satisfactory. In order to overcome the challenges encountered by COA, such as being stuck in the local optimal and insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, and chaos mutation. To evaluate the convergence accuracy, speed, and robustness of the modified crayfish optimization algorithm (MCOA), some simulation comparison experiments of 10 algorithms are conducted. Experimental results show that the MCOA achieved the minor Friedman test value in 23 test functions, CEC2014 and CEC2020, and achieved average superiority rates of 80.97%, 72.59%, and 71.11% in the WT, respectively. In addition, MCOA shows high applicability and progressiveness in five engineering problems in actual industrial field. Moreover, MCOA achieved 80% and 100% superiority rate against COA on CEC2020 and the fixed-dimension function of 23 benchmark test functions. Finally, MCOA owns better convergence and population diversity. [ABSTRACT FROM AUTHOR]

Aiming at the problems of incomplete path coverage and path redundancy in Autonomous Underwater Vehicle (AUV) path planning, an Ant Colony Path Planning Optimization Based on Opposition-Based Learning (ACPPO-OBL) is proposed. Firstly, Opposition-Based Learning (OBL) is introduced during the initialization phase of the ant colony. Moreover, the theoretical proof that ant colonies can be distributed near the optimal ant colony has also been proposed, indicating that the ACPPO-OBL algorithm has enhanced global search ability. Secondly, the coefficient for pheromone evaporation is revised. Besides, the proposed method involves a global pheromone update incorporating both best and worst reward mechanisms. Furthermore, it has been theoretically proven that the ACPPO-OBL algorithm has upper and lower bounds on the total pheromone concentration when searching for the optimal path. Additionally, an adaptive coefficient is incorporated into the heuristic function. The theoretical proof of the convergence of ACPPO-OBL has been established. As demonstrated in simulation experiments, ACPPO-OBL increases path coverage rates by 2–6 % and reduces path lengths by 6–11 % compared to ECDM planning. The ACPPO-OBL can be applied to cover irregular areas of various shapes and provides better coverage, improving the efficiency and stability of full-coverage paths in irregular areas. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimization Algorithm (GWO) is a member of the swarm intelligence algorithm family, which possesses the highlights of easy realization, simple parameter settings and wide applicability. However, in some large-scale application problems, the grey wolf optimization algorithm easily gets trapped in local optima, exhibits poor global exploration ability and suffers from premature convergence. Since grey wolf's update is guided only by the best three wolves, it leads to low population multiplicity and poor global exploration capacity. In response to the above issues, we design a multi-strategy collaborative grey wolf optimization algorithm (NOGWO). Firstly, we use a random walk strategy to extend the exploration scope and enhance the algorithm's global exploration capacity. Secondly, we add an opposition-based learning model influenced by refraction principle to generate an opposite solution for each population, thereby improving population multiplicity and preventing the algorithm from being attracted to local optima. Finally, to balance local exploration and global exploration and elevate the convergence effect, we introduce a novel convergent factor. We conduct experimental testing on NOGWO by using 30 CEC2017 test functions. The experimental outcomes indicate that compared with GWO and some swarm intelligence algorithms, NOGWO has better global exploration capacity and convergence accuracy. In addition, we also apply NOGWO to three engineering problems and an unmanned aerial vehicle path planning problem. The outcomes of the experiment suggest that NOGWO performs well in solving these practical problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic algorithm named the opposition-based cooperation search algorithm with Nelder–Mead (OCSANM). This enhanced algorithm builds upon the cooperation search algorithm (CSA) by incorporating opposition-based learning (OBL) and the Nelder–Mead simplex search method. The primary application of this algorithm is the design of a fractional-order proportional–integral–derivative (FOPID) controller for a buck converter system. A comprehensive evaluation is conducted using statistical boxplot analysis, nonparametric statistical tests and convergence response comparisons to assess the algorithm's performance and confirm its superiority over CSA. Furthermore, the FOPID-controlled buck converter system based on OCSANM is compared with two top-performing algorithms: one using a hybridized approach of Lévy flight distribution with simulated annealing (LFDSA) and the other employing the improved hunger games search (IHGS) algorithm. This comparison encompasses transient and frequency responses, performance indices and robustness analysis. The results reveal the notable advantages of the proposed OCSANM-based system, including 25.8% and 8.7% faster rise times, 26% and 8.8% faster settling times compared with the best-performing approaches, namely LFDSA and IHGS, respectively. In addition, the OCSANM-based system exhibits a 34.7% and 9.6% wider bandwidth than the existing approaches-based systems. Incorporating voltage and current responses of the buck converter's switched circuit with the OCSANM-based FOPID controller further underscores the algorithm's effectiveness. To provide a comprehensive assessment, the paper also compares the proposed approach's time and frequency domain responses with those of 17 other state-of-the-art approaches attempting to control buck converter systems similarly. These findings affirm the effectiveness of the OCSANM in designing FOPID controllers for buck converter systems. [ABSTRACT FROM AUTHOR]

Aiming to overcome faults of lower convergence accuracy and susceptibility to local optima in sparrow search algorithm(SSA), this paper proposed an enhanced sparrow search algorithm by adopting the mechanism based on relative distance and historical success rate, namely RHSSA. Firstly, RHSSA introduced a discoverer selection method that integrated fitness values and relative distance to make selected discoverers maintaining high quality and wider distribution in search space. Secondly, RHSSA adopted a reverse learning strategy that integrated weighted center of gravity during each search iteration of discovers in order to fully mining the high-quality location information in the search space and weakening discoverers' trend to gather towards the origin. Finally, RHSSA also used an adaptive selection operator based on historical success rate to dynamically select between Cauchy and Gaussian mutations to disturb the optimal solution to improve the algorithm's ability to jump out of local optimal. 12 functions were selected from the CEC2017 test function suit as the benchmark to evaluate RHSSA with five other improved sparrow search algorithms(AMSSA, SCSSA, SHSSA, ISSA, and CSSOA). The result of Friedman test based on experimental data shows that RHSSA can achieve the supreme performance among all evaluated algorithms. To futher verify effectiveness of the proposed improvement strategies, ablation experiments were conducted. The result illustrates that under the combination of all proposed improvement strategies, RHSSA ranks first in comprehensive optimization performance. [ABSTRACT FROM AUTHOR]

The flying foxes optimization (FFO) algorithm, as a newly introduced metaheuristic algorithm, is inspired by the survival tactics of flying foxes in heat wave environments. FFO preferentially selects the best-performing individuals. This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area. To address this issue, the paper introduces an opposition-based learning-based search mechanism for FFO algorithm (IFFO). Firstly, this paper introduces niching techniques to improve the survival list method, which not only focuses on the adaptability of individuals but also considers the population's crowding degree to enhance the global search capability. Secondly, an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality. Finally, to verify the superiority of the improved search mechanism, IFFO, FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions. The results prove that compared with other algorithms, IFFO is characterized by its rapid convergence, precise results and robust stability. [ABSTRACT FROM AUTHOR]

Opposition-based learning (OBL) is an optimization method widely applied to algorithms. Through analysis, it has been found that different variants of OBL demonstrate varying performance in solving different problems, which makes it crucial for multiple OBL strategies to co-optimize. Therefore, this study proposed a dynamic allocation of OBL in differential evolution for multi-role individuals. Before the population update in DAODE, individuals in the population played multiple roles and were stored in corresponding archives. Subsequently, different roles received respective rewards through a comprehensive ranking mechanism based on OBL, which assigned an OBL strategy to maintain a balance between exploration and exploitation within the population. In addition, a mutation strategy based on multi-role archives was proposed. Individuals for mutation operations were selected from the archives, thereby influencing the population to evolve toward more promising regions. Experimental results were compared between DAODE and state of the art algorithms on the benchmark suite presented at the 2017 IEEE conference on evolutionary computation (CEC2017). Furthermore, statistical tests were conducted to examine the significance differences between DAODE and the state of the art algorithms. The experimental results indicated that the overall performance of DAODE surpasses all state of the art algorithms on more than half of the test functions. Additionally, the results of statistical tests also demonstrated that DAODE consistently ranked first in comprehensive ranking.

Honey badger algorithm (HBA) is a recent swarm-based metaheuristic algorithm that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity and an imbalance between exploration and exploitation. In this paper, an improved honey badger algorithm called ODEHBA is proposed to improve the performance of basic HBA. Firstly, an improved orthogonal opposition-based learning technique is employed to assist population in escaping local optimum. Secondly, differential evolution is utilized to ensure the enrichment of population diversity and to enhance convergence speed. Finally, the exploration capability of ODEHBA is boosted by an equilibrium pool strategy. To validate the efficacy of proposed ODEHBA, it is compared with 13 well-known metaheuristic algorithms on CEC2022 benchmark test sets. Friedman test and Wilcoxon rank-sum test are utilized to assess the performance of ODEHBA. Furthermore, three engineering design problems and Internet of Vehicles (IoV) routing problem are applied to validate the capability of ODEHBA. The simulation results demonstrate that ODEHBA excels in solving complex numerical problems, engineering design, and IoV routing problems. This holds significant practical implications for cost reduction and improved resource utilization.