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Parameter estimation of photovoltaic (PV) models from experimental current versus voltage (I-V) characteristic curves acts a pivotal part in the modeling a PV system and optimizing its performance. Although many methods have been proposed for solving this PV model parameter estimation problem, it is still challenging to determine highly accurate and reliable solutions. In this paper, this problem is firstly transformed into an optimization problem, and an objective function (OF) is formulated to quantify the overall difference between the experimental and simulated current data. And then, to enhance the performance of original cuckoo search algorithm (CSA), a novel improved cuckoo search algorithm (ImCSA) is proposed, by combining three strategies with CSA. In ImCSA, a quasi-opposition based learning (QOBL) scheme is employed in the population initialization step of CSA. Moreover, a dynamic adaptation strategy is developed and introduced for the step size without Lévy flight step in original CSA. A dynamic adjustment mechanism for the fraction probability (Pa) is proposed to achieve better tradeoff between the exploration and exploitation to increase searching ability. Afterwards, the proposed ImCSA is used for solving the problem of estimating parameters of PV models based on experimental I-V data. Finally, the proposed ImCSA has been demonstrated on the parameter identification of various PV models, i.e., single diode model (SDM), double diode model (DDM) and PV module model (PMM). Experimental results indicate that the proposed ImCSA outperforms the original CSA and its superior performance in comparison with other state-of-the-art algorithms, and they also show that our proposed ImCSA is capable of finding the best values of parameters for the PV models in such effective way for giving the best possible approximation to the experimental I-V data of real PV cells and modules. Therefore, the proposed ImCSA can be considered as a promising alternative to accurately and reliably estimate parameters of PV models.

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.

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

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

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.

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions.

Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish, MCOA proposes an environmental renewal mechanism that uses water quality factors to guide crayfish to seek a better environment. In addition, integrating a learning strategy based on ghost antagonism into MCOA enhances its ability to evade local optimality. To evaluate the performance of MCOA, tests were performed using the IEEE CEC2020 benchmark function and experiments were conducted using four constraint engineering problems and feature selection problems. For constrained engineering problems, MCOA is improved by 11.16%, 1.46%, 0.08% and 0.24%, respectively, compared with COA. For feature selection problems, the average fitness value and accuracy are improved by 55.23% and 10.85%, respectively. MCOA shows better optimization performance in solving complex spatial and practical application problems. The combination of the environment updating mechanism and the learning strategy based on ghost antagonism significantly improves the performance of MCOA. This discovery has important implications for the development of the field of optimization. Graphical Abstract

The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions, it suffers from a slow convergence rate and low exploration capability. Therefore, in this paper, a modified ChOA is proposed to improve the exploration and exploitation capabilities of the ChOA. This improvement is performed using greedy search and opposition-based learning (OBL), respectively. In order to investigate the efficiency of the OBLChOA, the OBLChOA’s performance is evaluated by twenty-three standard benchmark functions, ten suit tests of IEEE CEC06-2019, randomly generated landscape, and twelve real-world Constrained Optimization Problems (IEEE COPs-2020) from a variety of engineering fields, including industrial chemical producer, power system, process design and synthesis, mechanical design, power-electronic, and livestock feed ration. The results are compared to benchmark optimizers, including CMA-ES and SHADE as high-performance optimizers and winners of IEEE CEC competition; standard ChOA; OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. OBLChOA and CMA-ES rank first and second among twenty-seven numerical test functions, respectively, with forty and eleven best results. In the 100-digit challenge, jDE100 achieves the highest score of 100, followed by DISHchain1e + 12, and OBLChOA achieves the fourth-highest score of 93. In total, eighteen state-of-the-art algorithms achieved the highest score in seven out of ten issues. Finally, OBLChOA and CMA-ES achieve the best performance in five and four real-world engineering challenges, respectively.