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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 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]

The beetle antennae search algorithm (BAS) is inspired by the search behavior of long-horned beetles and is widely used in optimization problems. However, in the typical BAS algorithm, there are problems such as the insufficient optimization ability of beetle individuals and the lack of consideration of historical dynamic information in the algorithm search process, resulting in sufferings from local optimum, low diversity, and imbalanced utilization, etc. To solve these problems, an improved beetle swarm antennae search algorithm based on multiple operators, dubbed MO-BSAS, is proposed. First, the elite opposition-based learning is used to initialize the beetle population, which improves the population diversity and optimization ability of the algorithm; then, the Lévy Flight strategy is used to improve the traditional beetle moving operator. In addition, a multi-operator search strategy determined by the damped sinusoidal probability factor is designed to balance the exploitation and exploration capabilities of the algorithm and speed up its convergence. The MO-BSAS algorithm is compared with BSA and 8 popular or state-of-the-art smart algorithms under 14 different benchmark functions, and the experimental results show that the MO-BSAS algorithm has a shorter convergence time and better convergence accuracy than the algorithms compared, which verifies the effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using the Friedman and Wilcoxon signed-rank test confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems. [ABSTRACT FROM AUTHOR]

Opposition-based learning (OBL) is often embedded in intelligent optimization algorithms to solve practical engineering and mathematical problems, but the combinatorial problems among different OBL variants are rarely studied. To this end, we propose a novel OBL variant based on the principle of optical imaging, which combines two novel types of quasi-opposite learning and extended opposite learning, called diversity-driven fused opposition learning (SQOBL). First, a density center based on a neighborhood model is proposed. Based on the rapid convergence of the centroid, combined the advantages of density and centroid to construct a double mean center (DMC) to replace the original center point in quasi-opposite learning based on the principle of refraction. Secondly, an extended opposite learning method based on optical refraction imaging is proposed. Diversity is then exploited to drive different opposing learning strategies at different stages of evolution, thus controlling the exploration and utilization of the algorithm. Finally, SQOBL was embedded in the PSO with eight others representative OBL variants to find the most optimal solution for a test suite. In addition, 8 novel intelligent optimization algorithms and the first three algorithms were selected to evaluate the performance of the latest CEC2022 benchmark test set and realistic constrained optimization problems. Experiments with 56 test functions and 3 real-world constraint optimization problems show that the proposed SQOBL has good integrative properties in CEC2015, CEC2017, CEC2020, and CEC2022 test suites. [ABSTRACT FROM AUTHOR]

To address the problems of slow convergence of the traditional whale optimization algorithm and the tendency to fall into local optimal solutions, an improved algorithm based on a combination of elite disturbance opposition-based learning and dynamic spiral updating (OWOA) is proposed, which improves the convergence speed of the algorithm and increases the probability of the algorithm jumping out of the local optimum to find the global optimum. First, an elite disturbance opposition-based learning strategy is used to initialize the whale population to ensure diversity of the population, and then, random perturbation of the elite whales increases the exploratory capacity of the population to avoid falling into local optimal solutions. Secondly, a dynamic spiral update strategy is used to dynamically adjust the shape of the spiral with the number of iterations, avoiding the algorithm from falling into a local optimum at the beginning of the iteration and improving the convergence speed of the algorithm at a later stage, which improves the ability of the algorithm to find the global optimum. The experimental results show that the OWOA algorithm, due to the introduction of the lite disturbance opposition-based learning strategy and dynamic spiral updating strategy, has higher convergence speed and accuracy on single-peak functions, multi-peak functions and multi-dimensional functions compared to other algorithms. Furthermore, good results have been obtained in the practical application of OPF. This shows that our improvements are effective. [ABSTRACT FROM AUTHOR]

As a new bionic algorithm, Spider Monkey Optimization (SMO) has been widely used in various complex optimization problems in recent years. However, the new space exploration power of SMO is limited and the diversity of the population in SMO is not abundant. Thus, this paper focuses on how to reconstruct SMO to improve its performance, and a novel spider monkey optimization algorithm with opposition-based learning and orthogonal experimental design (SMO3) is developed. A position updatingmethod based on the historical optimal domain and particle swarm for Local Leader Phase (LLP) andGlobal Leader Phase (GLP) is presented to improve the diversity of the population of SMO. Moreover, an opposition-based learning strategy based on self-extremum is proposed to avoid suffering from premature convergence and getting stuck at locally optimal values. Also, a local worst individual elimination method based on orthogonal experimental design is used for helping the SMO algorithm eliminate the poor individuals in time. Furthermore, an extended SMO3 named CSMO3 is investigated to deal with constrained optimization problems. The proposed algorithm is applied to both unconstrained and constrained functions which include the CEC2006 benchmark set and three engineering problems. Experimental results show that the performance of the proposed algorithm is better than three well-known SMO algorithms and other evolutionary algorithms in unconstrained and constrained problems. [ABSTRACT FROM AUTHOR]

Abstract Opposition-based learning (OBL) is often embedded in intelligent optimization algorithms to solve practical engineering and mathematical problems, but the combinatorial problems among different OBL variants are rarely studied. To this end, we propose a novel OBL variant based on the principle of optical imaging, which combines two novel types of quasi-opposite learning and extended opposite learning, called diversity-driven fused opposition learning (SQOBL). First, a density center based on a neighborhood model is proposed. Based on the rapid convergence of the centroid, combined the advantages of density and centroid to construct a double mean center (DMC) to replace the original center point in quasi-opposite learning based on the principle of refraction. Secondly, an extended opposite learning method based on optical refraction imaging is proposed. Diversity is then exploited to drive different opposing learning strategies at different stages of evolution, thus controlling the exploration and utilization of the algorithm. Finally, SQOBL was embedded in the PSO with eight others representative OBL variants to find the most optimal solution for a test suite. In addition, 8 novel intelligent optimization algorithms and the first three algorithms were selected to evaluate the performance of the latest CEC2022 benchmark test set and realistic constrained optimization problems. Experiments with 56 test functions and 3 real-world constraint optimization problems show that the proposed SQOBL has good integrative properties in CEC2015, CEC2017, CEC2020, and CEC2022 test suites.

The ideal gas molecular movement (IGMM) as a metaheuristic optimization method is a prominent option for solving optimization problems. However, in some complex cases, IGMM may possess premature convergence or get trapped in local optima. Therefore, to tackle these issues, this paper indicates a new modified IGMM algorithm named opposition‐based IGMM, which has been incorporated with opposition based learning (OBL), Cauchy mutation (CM), velocity clamping (VC), and mirror operator (MO) to enhance its performance. OBL, VC, and MO improve the convergence of IGMM, whereas CM assists IGMM to escape local optima. The effect of each strategy, OBL, CM, VC, and MO, on IGMM, is confirmed through 30 low and high‐dimensional benchmarks, including 23 well‐known mathematical problems and CEC2017 as complex test functions and three engineering problems. Analysis results represent that integration IGMM with OBL, CM, VC, and MO has the best performance among other IGMM variants and eventually improved IGMM in exploration, exploitation, accelerating convergence, and local optima avoidance. [ABSTRACT FROM AUTHOR]

This paper solves the bi-objective dynamic thermal power dispatch (DTPD) problem using an ameliorated salp swarm optimizer (ASSO). The bi-objective DTPD problem finds the optimum generation schedule of all the committed generating units for a time horizon of 24 h with variable demand for each time interval. It aims to reduce the operating cost and emission of pollutants. The advancement of the economic generation scheduling problem provides an hour's solution for the forecasted power demand. A forward approach is employed to find a global solution to the bi-objective DTPD problem. The price penalty is employed to unify the objectives defined as the average operating cost ratio to the emission of pollutants at thermal generations. Incremental and random methods compute price penalty. The generation schedule computed is usually restricted by the high and low economic generation limits and the ramp rate at which the unit responds over the nominal dispatch period, considering spinning reserves. A backtracking strategy handles ramp-rate restrictions imposed to ensure the feasibility of the solution. The heuristic search handles equality, and the replacement method handles inequality constraints. ASSO works on swarming actions of salp, colonial and solitary phases to produce salps employing opposition-based learning to improve diversity. Six opposition strategies are analyzed, and the best is selected using cardinal priority ranking. Four standard power test systems are examined to validate the solutions. The statistical analysis shows the outcome of the recommended algorithm that excels at the other challenging algorithms. [ABSTRACT FROM AUTHOR]

The jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a newly developed metaheuristic algorithm that solves complex and real-world optimization problems. The global exploration capability and robustness of the JS algorithm are strong, but the JS algorithm still has significant development space for solving complex optimization problems with high dimensions and multiple local optima. Therefore, in this study, an enhanced jellyfish search (EJS) algorithm is developed, and three improvements are made: (i) By adding a sine and cosine learning factors strategy, the jellyfish can learn from both random individuals and the best individual during Type B motion in the swarm to enhance optimization capability and accelerate convergence speed. (ii) By adding a local escape operator, the algorithm can skip the trap of local optimization, and thereby, can enhance the exploitation ability of the JS algorithm. (iii) By applying an opposition-based learning and quasi-opposition learning strategy, the population distribution is increased, strengthened, and more diversified, and better individuals are selected from the present and the new opposition solution to participate in the next iteration, which can enhance the solution's quality, meanwhile, convergence speed is faster and the algorithm's precision is increased. In addition, the performance of the developed EJS algorithm was compared with those of the incomplete improved algorithms, and some previously outstanding and advanced methods were evaluated on the CEC2019 test set as well as six examples of real engineering cases. The results demonstrate that the EJS algorithm can skip the trap of local optimization, can enhance the solution's quality, and can increase the calculation speed. In addition, the practical engineering applications of the EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and therefore, suggests future possible applications for solving such optimization problems. [ABSTRACT FROM AUTHOR]

The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency. [ABSTRACT FROM AUTHOR]

The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors. [ABSTRACT FROM AUTHOR]

This study presents a hybrid optimization technique to optimize stator and rotor slots of induction motor (IM) design for electric vehicle (EV) applications. The existing meta-heuristic optimization techniques for the IM design, such as genetic algorithm (GA) and particle swarm optimization (PSO), suffer premature convergence, exploration and exploitation imbalance, and computational burden. Therefore, this study proposes a new hybrid optimization technique called opposition-based jellyfish search optimization (OBJSO). This technique adopts opposition-based learning (OBL) into a jellyfish search optimization (JSO). Apart from that, a multi-objective formulation is derived to maximize the main performance indicators of EVs, including efficiency, breakdown torque, and power factor. The proposed OBJSO is used to solve the optimal design of stator and rotor slots based on the formulated multi-objective. The performance is compared with conventional optimization techniques, such as GA, PSO, and JSO. OBJSO outperforms three other optimization techniques in terms of average fitness by 2.2% (GA), 1.3% (PSO), and 0.17% (JSO). Furthermore, the convergence rate of OBJSO is improved tremendously, where up to 13.6% reduction in average can be achieved compared with JSO. In conclusion, the proposed technique can be used to help engineers in the automotive industry design a high-performance IM for EVs as an alternative to the existing motor. [ABSTRACT FROM AUTHOR]

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Multi-verse optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step is using opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models. [ABSTRACT FROM AUTHOR]

Butterfly optimization algorithm (BOA) is a relatively novel optimization technique for solving function optimization as well as real-world applications. However, the paramount challenge in BOA is that it is prone to stagnation in local optima. The purpose of this study is to balance the exploration and exploitation abilities of BOA when two new strategies are introduced. The dynamic inertia weight based on Logistic model as the first strategy is introduced to modify the position updating equation. Another strategy is the opposition-based learning. A new variant of BOA called BBOA based on these two strategies is proposed. Ten widely used benchmark test functions and 30 complex benchmarks from CEC 2014 are selected to verify the effectiveness of BBOA. The used benchmark problems are composed of unimodal, multimodal, rotated, shifted, hybrid and composite functions. The experimental results and analysis show that the proposed BBOA has better exploration ability than the conventional BOA when solving different characteristics functions. Finally, BBOA is applied to solve three real-world engineering applications and sixteen feature selection problems. The results demonstrate that BBOA can outperform other competitors in terms of the accuracy of solution on constrained engineering design and feature selection problems. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear PID (NLPID) controller is used to replace a traditional PID controller to overcome the influence of nonlinear factors in the computer numerical control (CNC) system. A particle swarm optimization based on a generalized opposition-based learning (G-PSO) algorithm is proposed to optimize the NLPID controller. The convergence speed and global optimization ability of the particle swarm optimization (PSO) algorithm are improved by introducing generalized opposition-based learning. The natural selection mutation is introduced into the G-PSO algorithm to further avoid the particles falling into local optimization. Different from the existing research, this paper designs a special fitness function according to the control objectives of improving system response speed and suppressing overshoot. By comparing the differential evolution (DE) algorithm, the ant lion optimizer (ALO) and the genetic algorithm (GA) through simulation, it is proven that the G-PSO algorithm has a faster convergence speed and better global optimization ability. Compared to Fuzzy PID and MRAC PID, G-PSO NLPID is shown to be more suitable for CNC systems. Additionally, it is proven through experiments that the rise time and settling time of the NLPID controller optimized by the G-PSO algorithm are 22.22% and 24.52% faster, respectively, than the traditional PID controller, and the system overshoot is successfully suppressed. [ABSTRACT FROM AUTHOR]

The prediction accuracy and generalization ability of extreme learning machine (ELM) are reduced by randomly generated weight and threshold before training. To solve these problems, an improved sparrow algorithm (ISSA)-ELM hybrid prediction model is proposed in this paper. Firstly, SSA is improved by the opposition-based learning (OBL), cosine inertia weights and Levy flight strategy. Secondly, unimodal and multimodal benchmark functions are used to verify the performance of ISSA. The test results show that ISSA has better optimization accuracy, stability and statistical properties than SSA, whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), gravity search algorithm (GSA), grasshopper optimization algorithm (GOA) and dragonfly algorithm (DA). Finally, ISSA is applied to optimize the weights and thresholds of ELM, and four regression datasets are used to test ISSA-ELM. The simulation results show that ISSA-ELM has better prediction accuracy, generalization ability and stability than SSA-ELM and ELM. [ABSTRACT FROM AUTHOR]

This paper introduces a modified slime mould algorithm to enhance the exploitation and exploration of the algorithm. As stated in no free lunch theorem no single algorithm can proficiently solve the optimization problems at hand. Hence, hybridization of more than one heuristic algorithm is employed to tackle the situation. The proposed strategy employs opposition-based learning and wavelet mutation-based strategy to the basic slime mould algorithm for exploring the search space while avoiding local optima. These methods are tested on standard benchmark functions, CEC-14 functions and different single and multiobjective thermal load dispatch problems including small, medium and large-scale problems while satisfying non-differentiable constraints namely valve point loading, prohibited operating zone and ramp rate limit which make objective function non-convex and discontinuous. The pareto dominance technique is employed to achieve the optimal solution in multiobjective problems. This paper also compares the performance of the proposed technique with other methods available in the literature for different economic load dispatch problems and emission dispatch problems. The Wilcoxon signed-rank test performed to confirm the competitiveness of the proposed method for independent samples. [ABSTRACT FROM AUTHOR]

The manta ray foraging optimizer (MRFO) is a novel nature-inspired optimization algorithm that simulates the foraging strategy and behavior of manta ray groups, i.e., chain, spiral, and somersault foraging. Although the native MRFO has revealed good competitive capability with popular meta-heuristic algorithms, it still falls into local optima and slows the convergence rate in dealing with some complex problems. In order to ameliorate these deficiencies of the MRFO, a new elite chaotic MRFO, termed the CMRFO algorithm, integrated with chaotic initialization of population and an opposition-based learning strategy, is developed in this paper. Fourteen kinds of chaotic maps with different properties are used to initialize the population. Thereby, the chaotic map with the best effect is selected; meanwhile, the sensitivity analysis of an elite selection ratio in an elite chaotic searching strategy to the CMRFO is discussed. These strategies collaborate to enhance the MRFO in accelerating overall performance. In addition, the superiority of the presented CMRFO is comprehensively demonstrated by comparing it with a native MRFO, a modified MRFO, and several state-of-the-art algorithms using (1) 23 benchmark test functions, (2) the well-known IEEE CEC 2020 test suite, and (3) three optimization problems in the engineering field, respectively. Furthermore, the practicability of the CMRFO is illustrated by solving a real-world application of shape optimization of cubic generalized Ball (CG-Ball) curves. By minimizing the curvature variation in these curves, the shape optimization model of CG-Ball ones is established. Then, the CMRFO algorithm is applied to handle the established model compared with some advanced meta-heuristic algorithms. The experimental results demonstrate that the CMRFO is a powerful and attractive alternative for solving engineering optimization problems. [ABSTRACT FROM AUTHOR]

Non-Line-of-Sight (NLOS) conditions are created by blocking the direct path between the transmitter and receiver, resulting in an increased signal propagation path. To mitigate the Time of Arrival (TOA) measured errors caused by the NLOS phenomenon in cellular radio positioning, we use the Maximum Likelihood (ML) estimation method in this work. The cost function of the ML estimator is usually a high-dimensional, nonlinear, and multimodal function, where standard deterministic optimization techniques cannot solve such problems in real-time and without significant computing resources. In this paper, effective metaheuristic algorithms based on the enhanced variants of Particle Swarm Optimization (PSO) are applied for the optimal solution of the ML problem and efficiently determine the mobile station location. Time-Varying Acceleration Coefficients (TVAC) are introduced into the standard PSO algorithm to enhance the global search and convergence properties. The resulting algorithm is known as PSO-TVAC. To further improve the performance of the metaheuristic optimization, we suggest adding Chaos Search (CS), Opposition-Based Learning (OBL), and TVAC strategy to the PSO process. The simulation results show that the proposed metaheuristic algorithm named the Chaotic Opposition-based PSO-TVAC (COPSO-TVAC) can reach the Generalized Cramer–Rao Lower Bound (GCRLB) and surpass the original PSO, PSO-TVAC, and the presented conventional optimization algorithms. [ABSTRACT FROM AUTHOR]

Background: In the process of fracture reduction, there are some errors between the actual trajectory and the ideal trajectory due to mechanism errors, which would affect the smooth operation of fracture reduction. To this end, based on self-developed parallel mechanism fracture reduction robot (FRR), a novel method to reduce the pose errors of FRR is proposed. Methods: Firstly, this paper analyzed the pose errors, and built the model of the robot pose errors. Secondly, mechanism errors of FRR were converted into drive bar parameter's errors, and the influence of each drive bar parameter on the robot pose error were analyzed. Thirdly, combining with Cauchy opposition-based learning and differential evolution algorithm (DE), an improved whale optimization algorithm (CRLWOA-DE) is proposed to compensate the end-effector's pose errors, which could improve the speed and accuracy of fracture reduction, respectively. Results: The iterative accuracy of CRLWOA-DE is improved by 50.74%, and the optimization speed is improved by 22.62% compared with the whale optimization algorithm (WOA). Meanwhile, compared with particle swarm optimization (PSO) and ant colony optimization (ACO), CRLWOA-DE is proved to be more accurate. Furthermore, SimMechanics in the software of MATLAB was used to reconstruct the fracture reduction robot, and it was verified that the actual motion trajectory of the CRLWOA-DE optimized kinematic stage showed a significant reduction in error in both the x-axis and z-axis directions compared to the desired motion trajectory. Conclusions: This study revealed that the error compensation in FRR reset process had been realized, and the CRLWOA-DE method could be used for reducing the pose error of the fracture reduction robot, which has some significance for the bone fracture and deformity correction. [ABSTRACT FROM AUTHOR]

In view of the shortcomings of the sparrow search algorithm (SSA) in solving complex optimization problems, such as slow convergence speed, severe population convergence and being easy to fall into local optimum, a multi-strategy improved sparrow search algorithm (MISSA) is proposed. First, the chaotic mapping and reverse learning mechanisms are applied to improve the quality of the initial population. Then, the learning strategy of particle swarm algorithm is introduced to improve the information communication ability of the population and balance the performances of global exploration and local development of the algorithm. Finally, the mutation and cross operations of the differential evolution algorithm are used to enhance the escape power from the local optimal value. Experiments with eight benchmark functions show that the proposed algorithm has better optimization performance and convergence efficiency. Furthermore, the proposed algorithm is applied to optimize the parameters of support vector regression (SVR) model and its effectiveness is demonstrated with five selected UCI datasets. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Harris hawks optimization algorithm converges slowly and is prone to local optimization, this paper proposes an improved Harris hawks optimization algorithm (IHHO) which combines mutually beneficial symbiosis and lens imaging learning. Firstly, the algorithm uses Tent chaotic map to initialize the population to increase the diversity of the population and improve the optimization performance of the algorithm. Secondly, in the exploration stage, the algorithm integrates the idea of mutually beneficial symbiosis and nonlinear inertia factor to enhance the exchange of population information and accelerate the convergence speed. Then, the algorithm uses lens imaging reverse learning strategy to perturb and mutate the Harris hawks position with a certain probability to improve the ability of the algorithm to jump out of the local optimum. Finally, the simulation results of 16 benchmark test functions show that IHHO has faster convergence speed, higher precision and stronger robustness compared with the other five algorithms. At the same time, IHHO is applied to the problem of image segmentation, and the simulation results verify the feasibility of the algorithm in practical engineering applications. [ABSTRACT FROM AUTHOR]

To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems. [ABSTRACT FROM AUTHOR]

To solve the problem of premature in traditional multi-objective particle swarm optimization algorithms, an opposition-based multi-objective particle swarm optimization algorithm was proposed based on tripartite competition mechanism ( MOPSO-TCOL). The tripartite competitors were selected from the current population in each generation to guide the population evolution in MOPSO-TCOL, which could effectively reduce the computational cost. In each competition, three particles were randomly selected from the population for comparison and updated by different strategies, which could help to maintain the diversity of population. A novel progressive particle update strategy was proposed based on opposition based learning ( OBL). Some particles were updated by OBL strategy to avoid the algorithm from falling into local optima, and other particles were updated by learning from the specified better particles to improve the convergence. The experimental results on 14 benchmark test instances verify the superiority of the proposed algorithm in terms of diversity and convergence over 8 multi-objective optimization algorithms, and it has faster convergence rate. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technologies, fog computing has emerged as an extension to the cloud computing that relies on fog nodes with distributed resources at the edge of network. Fog nodes offer computing and storage resources opportunities to resource-less IoT devices which are not capable to support IoT applications with computation-intensive requirements. Furthermore, the closeness of fog nodes to IoT devices satisfies the low-latency requirements of IoT applications. However, due to the high IoT task offloading requests and fog resource limitations, providing an optimal task scheduling solution that considers a number of quality metrics is essential. In this paper, we address the task scheduling problem with the aim of optimizing the time and energy consumption as two QoS parameters in the fog context. First, we present a fog-based architecture for handling the task scheduling requests to provide the optimal solutions. Second, we formulate the task scheduling problem as an Integer Linear Programming (ILP) optimization model considering both time and fog energy consumption. Finally, we propose an advanced approach called Opposition-based Chaotic Whale Optimization Algorithm (OppoCWOA) to enhance the performance of the original WOA for solving the modelled task scheduling problem in a timely manner. The efficiency of the proposed OppoCWOA is shown by providing extensive simulations and comparisons with the original WOA and some existing meta-heuristic algorithms such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear PID (NLPID) controller is used to replace a traditional PID controller to overcome the influence of nonlinear factors in the computer numerical control (CNC) system. A particle swarm optimization based on a generalized opposition-based learning (G-PSO) algorithm is proposed to optimize the NLPID controller. The convergence speed and global optimization ability of the particle swarm optimization (PSO) algorithm are improved by introducing generalized opposition-based learning. The natural selection mutation is introduced into the G-PSO algorithm to further avoid the particles falling into local optimization. Different from the existing research, this paper designs a special fitness function according to the control objectives of improving system response speed and suppressing overshoot. By comparing the differential evolution (DE) algorithm, the ant lion optimizer (ALO) and the genetic algorithm (GA) through simulation, it is proven that the G-PSO algorithm has a faster convergence speed and better global optimization ability. Compared to Fuzzy PID and MRAC PID, G-PSO NLPID is shown to be more suitable for CNC systems. Additionally, it is proven through experiments that the rise time and settling time of the NLPID controller optimized by the G-PSO algorithm are 22.22% and 24.52% faster, respectively, than the traditional PID controller, and the system overshoot is successfully suppressed.

The optimization-based scheduling algorithms used for scheduling workflows in cloud computing environments may easily get trapped in local optima, especially in the beginning of their simulation processes because of some limitations in their exploration methods. Moreover, the performance of some optimization-based scheduling algorithms may severely degrade when dealing with medium- or large-size scheduling problems. The Island-based Cuckoo Search with highly disruptive polynomial mutation (iCSPM) algorithm is a parallel version of the Cuckoo Search (CS) algorithm. The iCSPM algorithm incorporates the island model into CS and uses an exploration function based on the highly disruptive polynomial mutation. It has been empirically proven that iCSPM performs better than popular optimization algorithms (e.g., CS and island-based Genetic algorithm). This paper presents a variation of iCSPM called Discrete iCSPM with opposition-based learning strategy (DiCSPM) for scheduling workflows in cloud computing environments based on two objectives: computation and data transmission costs. DiCSPM includes two new features compared to iCSPM. First, it uses the opposition-based learning approach (OBL) in the initialization step at the level of islands, where each island in the island model contains the opposite population of another island. Second, the smallest position value method is used in the DiCSPM algorithm to determine the correct values of the decision variables in the candidate solutions. The proposed algorithm was experimentally evaluated and compared to well-known scheduling algorithms [Best Resource Selection, Particle Swarm Optimization (PSO) and Grey Wolf Optimizer] using two types of workflows: balanced and imbalanced workflows. The overall experimental and statistical results indicate that DiCSPM provides solutions for the scheduling problem of workflows in cloud computing environment faster than the other compared algorithms. Moreover, DiCSPM was evaluated and compared to state-of-the-art algorithms, namely PSO, binary PSO and discrete binary cat swarm optimization using scientific workflows of different sizes using WorkflowSim. The obtained results suggest that DiCSPM provides the best makespan compared to the other algorithms. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population based meta-heuristic search technique that has been widely applied to deal with various optimization problems. However, like other stochastic methods, PSO also encounters the problems of entrapment into local optima and premature convergence in solving complex multimodal problems. To tackle these issues, a diversity-guided multi-mutation particle swarm optimizer (abbreviated as DMPSO) is presented in this paper. To start with, the chaos opposition-based learning (OBL) is employed to yield high-quality initial particles to accelerate the convergence speed of DMPSO. Followed by, the self-regulating inertia weight is leveraged to strike a balance between the exploration and exploitation in the search space. After that, three different kinds of mutation strategies (gaussian, cauchy and chaotic mutations) are used to maintain the potential diversity of the whole swarm based on an effective diversity-guided mechanism. In particular, an auxiliary velocity-position update mechanism is exclusively applied to the global best particle that can effectively guarantee the convergence of the DMPSO. Finally, extensive experiments on a set of well-known unimodal and multimodal benchmark functions demonstrate that DMPSO outperforms most of the other tested PSO variants in terms of both the solution quality and its efficiency.

Moth-Flame Optimization (MFO) algorithm is a new population-based meta-heuristic algorithm for solving global optimization problems. Flames generation and spiral search are two key components that affect the performance of MFO. To improve the diversity of flames and the searching ability of moths, an improved Moth-Flame Optimization (IMFO) algorithm is proposed. The main features of the IMFO are: the flames are generated by orthogonal opposition-based learning (OOBL); the modified position updating mechanism of moths with linear search and mutation operator. To evaluate the performance of IMFO, the IMFO algorithm is compared with other 20 algorithms on 23 benchmark functions and IEEE (Institute of Electrical and Electronics Engineers) CEC (Congress on Evolutionary Computation) 2014 benchmark test set. The comparative results show that the IMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Moreover, the IMFO is also used to solve three engineering optimization problems, and it is compared with other well-known algorithms. The comparison results show that the IMFO algorithm can improve the global search ability of MFO and effectively solve the practical engineering optimization problems. [ABSTRACT FROM AUTHOR]

Nature-inspired algorithms have been proved to be very powerful methods for complex numerical optimization problems. Quantum-behaved particle swarm optimization (QPSO) is a typical member of nature-inspired algorithms, and it is a simple and effective population-based technique used in numerical optimization. Despite its efficiency and wide use, QPSO suffers from premature convergence and poor balance between exploration and exploitation in solving complex optimization problems. To address these issues, a new evolutionary technique called generalized space transformation search is proposed, and then, we introduce an improved quantum-behaved particle swarm optimization algorithm combined with this new technique in this study. The proposed generalized space transformation search is based on opposition-based learning and generalized opposition-based learning, which can not only improve the exploitation of the current search space but also strengthen the exploration of the neighborhood of the current search space. The improved quantum-behaved particle swarm optimization algorithm employs generalized space transformation search for population initialization and generation jumping. A comprehensive set of 16 well-known unconstrained benchmark functions is employed for experimental verification. The contribution of the generalized space transformation search is empirically verified, and the influence of dimensionality is also investigated. Besides, the improved quantum-behaved particle swarm optimization algorithm is also compared with some typical extensions of QPSO and several competitive meta-heuristic algorithms. Such comparisons suggest that the improved quantum-behaved particle swarm optimization algorithm may lead to finding promising solutions compared to the other algorithms. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) is a recently developed meta-heuristic optimization algorithm based on hunting behavior of Harris hawks. Similar to other meta-heuristic algorithms, HHO tends to be trapped in low diversity, local optima and unbalanced exploitation ability. In order to improve the performance of HHO, a novel quasi-reflected Harris hawks algorithm (QRHHO) is proposed, which combines HHO algorithm and quasi-reflection-based learning mechanism (QRBL) together. The improvement includes two parts: the QRBL mechanism is introduced firstly to increase the population diversity in the initial stage, and then, QRBL is added in each population position update to improve the convergence rate. The proposed method will also be helpful to control the balance between exploration and exploitation. The performance of QRHHO has been tested on twenty-three benchmark functions of various types and dimensions. Through comparison with the basic HHO, HHO combined with opposition-based learning mechanism and HHO combined with quasi-opposition-based learning mechanism, the results demonstrate that QRHHO can effectively improve the convergence speed and solution accuracy of the basic HHO and two variants of HHO. At the same time, QRHHO is also better than other swarm-based intelligent algorithms. [ABSTRACT FROM AUTHOR]

To overcome drawbacks of grey wolf optimization algorithm (GWO), such as low convergence accuracy and easily trapping in local optimum, this paper proposes a hybridgrey wolf optimization (HGWO) algorithm and applies it to the robot path planning CRPP) problem. Firstly, HGWO uses the opposition-based learning met hod to generate initial population with high qualities. Secondly, the algorithm incorporates its historical information into the individual update method, so as to guide the population evolution. Meanwhile, an elite opposition strategy is applied to explore the space of elite solutions in current population, in order to strengthen the algorithm's exploitation ability. In addition, HGWO adopts the spline interpolation technique to guarantee convergence accuracy and to reduce the optimization difficulty of RPP. Finally, a comparative experiment of function optimization and path planning is carried out. The experimental results show that HGWO has good solution accuracy and robustness. [ABSTRACT FROM AUTHOR]

Success behind nature inspired evolutionary metaheuristic algorithms lies in its seemly combination of operator's castoff for smooth balance between exploration and exploitation. The deficit in such combination leads to untimely convergence of an algorithm, simultaneously failed to attain global optimum by stocking in local optimum. This work represents atypical algorithm termed as OBL-MO-SHO to improve the performance of existing SHO. To deal with more intricate realistic problems and to enhance the explorative and exploitative strength of SHO, we have integrated the oppositional learning concept with mutation operator. The proposed algorithm OBL-MO-SHO (oppositional spotted hyena optimizer with mutation operator) reveals promising performance in terms of achieving global optimum and superior convergence rate which confirms its improved exploration and exploitation capability within searching region. To establish competency of proposed OBL-MO-SHO algorithm the same is appraised by means of standard functions set belongs to IEEE CEC 2017. The efficacy of said method has been proven by means of various performance metrics and the outcomes also compared with state-of-the-art algorithms. To scrutinize its uniqueness statistically, Friedman and Holms test has been performed as one non-parametric test. Additionally as an application to unravel real world intricate difficulties the said OBL-MO-SHO algorithm has been castoff to train wavelet neural network by considering datasets selected from UCI depository. The reported results unveils that the evolved OBL-MO-SHO might be one potential algorithm for enlightening different optimization difficulties effectively. [ABSTRACT FROM AUTHOR]

Solar energy seems to be most sustainable way which can conserve the environment from negative impacts caused by electrical power generation from fossil fuels. For achieving the utmost power in output, photovoltaic (PV) array should operate on its optimal power point. When PV arrays are subjected to a partial shading state, several local peaks are experienced by power-voltage performance curve of PV system which significantly trims down the efficiency of complete system. So it becomes vital to locate the global peak of PV system for harvesting the upper limit power for better performance. This paper presents an improved grey wolf optimization (IGWO) technique which employs the concepts of parallel grey wolf optimization and opposition-based learning to trace the GMPP under partial shading situation and also figure out the performance for gradually varying with irradiance and temperature for PV system. This IGWO-based MPPT scheme has been implemented in MATLAB simulation environment. The outcome of the simulation is verified by the past-proposed MPPT techniques namely perturb and observe (P&O) and particle swarm optimization (PSO). It has been found from the simulation results that the performance of the suggested IGWO based MPPT design is advanced than the P&O and PSO techniques in terms of tracking speed, steady-state oscillation under partial shading conditions (PSCs) and accuracy. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a population-based algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly when optimization problems are subjected to noise. PSO is strongly influenced by its previous best particles and global best one, which may lead to premature convergence and fall into local optima. This also holds true for various PSO variants dealing with optimization problems in noisy environments. Opposition-based learning (OBL) is well-known for its ability to increase population diversity. In this paper, we propose hybrid PSO algorithms that introduce OBL into PSO variants for improving the latter's performance. The proposed hybrid algorithms employ probabilistic OBL for a swarm. In contrast to other integrations of PSO and OBL, we select the top fittest particles from the current swarm and its opposite swarm to improve the entire swarm's fitness. Experiments on 20 benchmark functions subject to different levels of noise show that the proposed hybrid PSO algorithms outperform their counterpart PSO variants as well as composite differential evolution in most cases.

Grey wolf optimization algorithm (GWO) is a new swarm intelligence optimization algorithm proposed in recent years. Because of few control parameters and easy implementation, GWO is widely used in many fields. Compared with other common swarm optimization algorithms, it is more suitable for the global optimization problems. Nevertheless, the algorithm still has the shortcoming of low accuracy and slow convergence speed. In this paper, a novel hybrid optimization algorithm named MDE-GWO is proposed. Firstly, a JADE with opposition-based learning strategy algorithm (MDE) is embedded in GWO to enhance the ability of avoiding a local optimum. Notably, by introducing the opposition-based learning strategy, the search ability of the improved algorithm is increased greatly. Additionally, in order to balance the global and local search capabilities and speed up the convergence of the GWO algorithm, a leader moving-rate strategy is put forward. 28 typical benchmark functions are utilized to test the performance of the improved algorithm. The experimental results show that MDE-GWO has stronger advantages in search accuracy, stability and convergence speed in most cases. [ABSTRACT FROM AUTHOR]

Moth flame optimization (MFO) algorithm proves to be an excellent choice for numerical optimization. However, for some complex objectives, MFO may get trapped in local optima or suffer from premature convergence. In order to overcome these issues, an improved MFO-based algorithm, called opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling (OMFO), is presented. The proposed method integrates opposition-based learning (OBL) with Cauchy mutation (CM) and evolution boundary constraint handling (EBCH) technique with MFO to improve its performance. OBL and EBCH improve the convergence of MFO, while CM helps MFO to escape local optima. The effect of each method (OBL, CM, EBCH) on MFO is validated using 18 benchmark functions and two constrained real-world problems. Simulation results indicate that opposition-based MFO integrated with Cauchy mutation and EBCH has the best performance among the MFO variants. The OMFO algorithm is also compared with various algorithms in the literature and provides competitive results in terms of increased exploitation and exploration capability, improved convergence and local optima avoidance. [ABSTRACT FROM AUTHOR]

A recently established swarm-based algorithm, namely, Mountain Gazelle Optimizer (MGO) which draws inspiration from social structure and hierarchy of wild mountain gazelles is competitive for solving optimization problems. However, the MGO has some drawbacks: when dealing with higher dimensions, early iterations could become stuck in suboptimal search area. It would be difficult for the MGO to abandon the local optimal solution if the early best solutions neglect the relevant search space. Therefore, to overcome these limitations, this paper offers an Evolved Opposition-based Learning (EOBL) mechanism which helps the algorithm to jump out of the local optima while accelerating the convergence speed. This novel mechanism is incorporating with MGO to propose Evolved Opposition-based Mountain Gazelle Optimizer (EOBMGO). The experiments are conducted with CEC2005 and CEC2019 benchmark functions, along with seven engineering challenges to examine the performance of the proposed EOBMGO. Furthermore, the statistical tests, like the t-test and Wilcoxon rank-sum test, are verified and demonstrate that the proposed EOBMGO outperforms the existing top-performing algorithms. The outcomes indicated that the proposed technique may be seen as an efficient and successful approach for complex optimization challenges. • Evolved Opposition-based learning (EOBL) is incorporated in MGO to propose EOBMGO. • EOBL is inspired by Opposition-based Learning (OBL). • EOBMGO is employed to solve different benchmark functions, and engineering problems. • EOBMGO generates excellent results compared to other algorithms. • EOBMGO maintains proper balance between exploration and exploitation search. [ABSTRACT FROM AUTHOR]