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opposition-based learning Harris hawks optimization with steepest convergence for engineering design problems

JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-52页

作者： Zhao, Yanfen Liu, Hao Univ Sci & Technol Liaoning Sch Sci Anshan 114051 Liaoning Peoples R China

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.

关键词： Swarm intelligence Harris hawks optimization opposition-based learning Steepest convergence Escaping energy factor

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Enhanced coati optimization algorithm using elite opposition-based learning and adaptive search mechanism for feature selection (May, 10.1007/s13042-024-02222-3, 2024)

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2025年第1期16卷 395-395页

作者： Qtaish, Amjad Braik, Malik Albashish, Dheeb Alshammari, Mohammad T. Alreshidi, Abdulrahman Alreshidi, Eissa Jaber Univ Hail Dept Informat & Comp Sci Hail Saudi Arabia Appl Sci Private Univ Accounting Dept Amman Jordan Al Balqa Appl Univ Comp Sci Dept Salt Jordan

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

关键词： Coati optimization algorithm Feature selection opposition-based learning Optimization

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Improved material generation algorithm by opposition-based learning and laplacian crossover for global optimization and advances in real-world engineering problems

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MATERIALS TESTING 2025年第4期67卷 737-746页

作者： Mehta, Pranav Kumar, Sumit Sait, Sadiq M. Yildiz, Betuel S. Yildiz, Ali Riza Bursa Uludag Univ Dept Mech Engn Bursa Turkiye Dharmsinh Desai Univ Dept Mech Engn Nadiad Gujarat India Univ Tasmania Launceston TAS Australia King Fahd Univ Petr & Minerals Dhahran Saudi Arabia

The current study aims to utilize a unique hybrid optimizer called oppositional-based learning and laplacian crossover augmented material generation algorithm (MGA-OBL-LP) to solve engineering design problems. The oppositional-based learning and laplacian crossover approaches are used to address the local optima trap weakness of a recently discovered MGA algorithm that has been added to the fundamental MGA structure. The proposed hybridization strategy aimed to make it easier to improve the exploration-exploitation behavior of the MGA algorithm. The performance of the proposed hybridized algorithm was compared with other notable metaheuristics collected from the literature for four constrained engineering design problems in order to determine whether it would be practical in real-world applications. A comparison analysis is undertaken to confirm the MGA-OBL-LP algorithm's competence in terms of solution quality and stability, and it is discovered to be robust in addressing difficult practical problems.

关键词： material generation algorithm opposition-based learning spring design spur gear design optimization

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A novel cheetah optimizer hybrid approach based on opposition-based learning (OBL) and diversity metrics

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COMPUTING 2025年第2期107卷 1-58页

作者： Cuevas, Erik Barba, Oscar Escobar, Hector Univ Guadalajara Dept Electrophoton Engn CUCEI Ave Revolut 1500 Guadalajara 44430 Mexico Univ Guadalajara Dept Innovat Based Informat & Knowledge CUCEI Ave Revoluc 1500 Guadalajara 44430 Mexico

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.

关键词： Cheetah optimizer Diversity opposition-based learning Exploration and exploitation

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Dynamic step opposition-based learning sparrow search algorithm for UAV path planning

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第1期28卷 1-15页

作者： He, Yong Wang, Mingran Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Hunan Peoples R China

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.

关键词： Sparrow search algorithm Path planning Good point set opposition-based learning

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Boosting multi-objective aquila optimizer with opposition-based learning for large-scale time–cost trade-off problems

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Asian Journal of Civil Engineering 2025年第5期26卷 2179-2188页

作者： Baltaci, Yusuf Civil Engineering Department Karadeniz Technical University Trabzon 61080 Turkey

This study presents an enhanced version of the Aquila optimizer (AO), known as the opposition-based aquila optimizer (OBAO), which incorporates opposition-based learning (OBL) to enhance performance. By considering both current solutions and their opposites, OBL expands the search space, increasing the chances of avoiding local optima and identifying superior solutions. Additionally, OBL replaces the expanded and narrowed exploitation methods of the original AO, reducing computational complexity and enhancing the efficiency of the proposed model. The proposed OBAO is applied to a large-scale time–cost trade-off problems (TCTP) with 630 activities, demonstrating its capability to efficiently achieve optimal or near-optimal solutions. Comparative assessments against advanced optimization algorithms, including teaching learning-based optimization (TLBO), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and plain AO indicate that OBAO achieves better solutions in terms of number of objective function evaluations (NFE) and hypervolume (HV) indicator. The findings suggest that OBAO is a promising alternative for optimizing large-scale construction projects in construction management field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquila optimization algorithm Construction management opposition-based learning Pareto-front solution Time–cost-trade-off problems

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opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0-1 knapsack problem

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 67卷 454-468页

作者： Feng, Yanhong Wang, Gai-Ge Dong, Junyu Wang, Ling Hebei GEO Univ Sch Informat Engn Shijiazhuang 050031 Hebei Peoples R China Ocean Univ China Dept Comp Sci & Technol Qingdao 266100 Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Jilin Peoples R China Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Monarch butterfly optimization (MBO) has become an effective optimization technique for function optimization and combinatorial optimization. In this paper, a generalized opposition-based learning (OBL) monarch butterfly optimization with Gaussian perturbation (OMBO) is presented, in which OBL strategy is used on half individuals of the population in the late stage of evolution and Gaussian perturbation acts on part of the individuals with poor fitness in each evolution. OBL guarantees the higher convergence speed of OMBO and Gaussian perturbation avoids to be stuck at a local optimum. In order to test and verify the effectiveness of the proposed method, three groups of 15 large-scale 0-1 KP instances from 800 to 2000 dimensions are used in our studies. The experimental results indicate that OMBO can find high-quality solutions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Monarch butterfly optimization opposition-based learning Gaussian perturbation 0-1 knapsack problems

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opposition-based learning inspired particle swarm optimization (OPSO) scheme for task scheduling problem in cloud computing

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第10期12卷 9855-9875页

作者： Agarwal, Mohit Srivastava, Gur Mauj Saran Sharda Univ Sch Engn & Technol Dept Comp Sci & Engn Greater Noida 201306 Uttar Pradesh India Dayalbagh Educ Inst Dept Phys & Comp Sci Agra 282002 Uttar Pradesh India

The problem of scheduling of tasks in distributed, heterogeneous, and multiprocessing computing environment like grid and cloud computing is considered as one of the most important issue from research perspective. As the performance of such kind of systems is highly depends upon the way, how tasks are allocated among the multiple processing units for their efficient execution. The underlying objective of any task scheduling mechanism is to minimize the overall makespan for the execution of given set of jobs/tasks and computing machines. Scheduling of tasks in cloud computing falls in the class of NP-hard optimization problem. As a result, many meta-heuristic algorithms have been applied and tested to solve this problem but still lot of scope is there for the better strategies. The characteristic of the good algorithm is that it must be adaptable to the dynamic environment. Through this paper, we are proposing task scheduling mechanism based on particle swarm optimization (PSO) in which opposition-based learning technique is used to avoid premature convergence and to accelerate the convergence of standard PSO and compared same with the well-established task scheduling strategies based on PSO, mPSO (modified PSO), genetic algorithm GA, max-min, minimum completion time and minimum execution time. The results obtained for the various class of experiments clearly establish that the proposed opposition-based learning inspired particle swarm optimization based scheduling strategy performs better in comparison to its peers which are taken into the consideration.

关键词： Cloud computing Distributed system Makespan Particle swarm optimization Task scheduling opposition-based learning

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opposition-based learning in shuffled frog leaping: An application for parameter identification

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INFORMATION SCIENCES 2015年第0期291卷 19-42页

作者： Ahandani, Morteza Alinia Alavi-Rad, Hosein Islamic Azad Univ Langaroud Branch Dept Elect Engn Langaroud Iran Islamic Azad Univ Langaroud Branch Young Researchers Club Langaroud Iran

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled frog leaping (SFL). The SFL divides a population into several memeplexes and then improves each memeplex in an evolutionary process. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the SFL which employ on one hand the OBL to accelerate the SFL without making premature convergence and on the other hand use the OBL strategy to diversify search moves of SFL. Four versions of SFL algorithm are proposed by incorporating the OBL and the SFL. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments are performed on parameter identification problems. The obtained results demonstrate that incorporating the opposition-based strategy and SFL performed in a proper way is a good idea to enhance performance of SFL. Two versions of opposition-based SFL outperform their pure competitor i.e., SFL in terms of all aspects on all problems but two other versions of SFL obtained a worse performance than the pure SFL. Also some performance comparisons of the proposed algorithms with some other algorithms reported in the literature confirm a significantly better performance of our proposed algorithms. Also in final part of the comparison study, a comparison of the proposed algorithm in this study in respect to other algorithms on CEC05 functions demonstrates a completely comparable performance of proposed algorithm. (C) 2014 Elsevier Inc. All rights reserved.

关键词： opposition-based learning Shuffled frog leaping Memeplex Parameter identification

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opposition-based learning in the shuffled differential evolution algorithm

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SOFT COMPUTING 2012年第8期16卷 1303-1337页

作者： Ahandani, Morteza Alinia Alavi-Rad, Hosein Islamic Azad Univ Langaroud Branch Dept Elect Engn Langaroud Iran

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.

关键词： opposition-based learning Shuffled differential evolution Memeplex Premature convergence

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