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The Walrus Optimization (WO) algorithm, as an emerging metaheuristic algorithm, has shown excellent performance in problem-solving, however it still faces issues such as slow convergence and susceptibility to getting trapped in local optima. To this end, the study proposes a novel WO enhanced by quasi-oppositional-based learning and chaotic local search mechanisms, called QOCWO. The study aims to prevent premature convergence to local optima and enhance the diversity of the population by integrating the quasi-oppositional-based learning mechanism into the original Walrus Optimization (WO) algorithm, thereby improving the global search capability and expanding the search range. Additionally, the chaotic local search mechanism is introduced to accelerate the convergence speed of WO. To test the capabilities, the QOCWO algorithm is applied to the 23 standard functions and compared with seven other algorithms. Furthermore, the Wilcoxon rank-sum test is utilized to evaluate the significance of the results, which demonstrates the superior performance of the proposed algorithm. To assess the practicality in solving real-world problems, the QOCWO is applied to two engineering design issues, and the results indicated that QOCWO achieved lower costs compared to other algorithms.

Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal and multimodal optimization problems. However, the convergence speed and optimization performance of BWO still have some performance deficiencies when solving complex multidimensional problems. Therefore, this paper proposes a hybrid BWO method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive and spiral predation strategy, and Nelder-Mead simplex search method (NM). Firstly, in the initialization phase, the QOBL strategy is introduced. This strategy reconstructs the initial spatial position of the population by pairwise comparisons to obtain a more prosperous and higher quality initial population. Subsequently, an adaptive and spiral predation strategy is designed in the exploration and exploitation phases. The strategy first learns the optimal individual positions in some dimensions through adaptive learning to avoid the loss of local optimality. At the same time, a spiral movement method motivated by a cosine factor is introduced to maintain some balance between exploration and exploitation. Finally, the NM simplex search method is added. It corrects individual positions through multiple scaling methods to improve the optimal search speed more accurately and efficiently. The performance of HBWO is verified utilizing the CEC2017 and CEC2019 test functions. Meanwhile, the superiority of HBWO is verified by utilizing six engineering design examples. The experimental results show that HBWO has higher feasibility and effectiveness in solving practical problems than BWO and other optimization methods.

This research work projects a novel quasi-oppositional chaotic student psychology-based optimization (SPBO) (QOCSPBO) algorithm for solving global optimization problems. To tackle the identified flaws of the standard SPBO, the proffered QOCSPBO algorithm combines two search strategies within the standard SPBO framework. The obtained outcomes exhibit that the proposed QOCSPBO algorithm outperforms SPBO and recently published algorithms in optimizing a set of well-known benchmark test functions. The projected QOCSPBO attains the optimal site and size of distributed generation and shunt capacitors in two radial distribution systems contemplating different types load models at three load levels. The obtained results prove that the recommended method can be highly suitable in solving real-time power system optimization problems with constrained and unknown search space.

Global optimization problems have been a research topic of great interest in various engineering applications among which neural network algorithm (NNA) is one of the most widely used methods. However, it is inevitable for neural network algorithms to plunge into poor local optima and convergence when tackling complex optimization problems. To overcome these problems, an improved neural network algorithm with quasi-oppositional-based and chaotic sine-cosine learning strategies is proposed, that speeds up convergence and avoids trapping in a local optimum. Firstly, quasi-oppositional-based learning facilitated the exploration and exploitation of the search space by the improved algorithm. Meanwhile, a new logistic chaotic sine-cosine learning strategy by integrating the logistic chaotic mapping and sine-cosine strategy enhances the ability that jumps out of the local optimum. Moreover, a dynamic tuning factor of piecewise linear chaotic mapping is utilized for the adjustment of the exploration space to improve the convergence performance. Finally, the validity and applicability of the proposed improved algorithm are evaluated by the challenging CEC 2017 function and three engineering optimization problems. The experimental comparative results of average, standard deviation, and Wilcoxon rank-sum tests reveal that the presented algorithm has excellent global optimality and convergence speed for most functions and engineering problems.

This article proffers a novel quasi-oppositional chaotic Harris hawk’s optimization (HHO) (QOCHHO) algorithm for interpreting global optimization problems. In the proposed QOCHHO algorithm, quasi-opposition based learning (QOBL) and chaotic local search (CLS) approaches are integrated with the basic HHO for better quality of solution and faster convergence. The idea of QOBL assists to explore new regions of the search space and offers superior exploration. Again, CLS guides the search process nearby the most favorable regions of the search space yielding superior exploitation. Thus, a superior balance between the exploration and the exploitation holds in the case of QOCHHO making this newly projected algorithm more robust as correlated to the HHO algorithm. To demonstrate and validate effectiveness of the suggested QOCHHO algorithm, twenty-nine benchmark test functions of various categories, varied complexities ( i.e., unimodal, multimodal, fixed dimension and composite functions) and different dimensions ( i.e., 30 and 100) are used for simulation experiments. The simulation results attained by the projected QOCHHO algorithm are compared with the results obtained by recently surfaced HHO and other state-of-the-art algorithms ( i.e., particle swarm optimization, moth-flame optimization algorithm, grey wolf optimizer, sine cosine algorithm, salp swarm algorithm, whale optimization algorithm and multi verse optimization algorithm). The outcomes of the benchmark test functions evidence that the anticipated QOCHHO algorithm is able to offers better outcomes in terms of improved exploration, local optima circumvention and faster convergence characteristics. The proposed QOCHHO algorithm is further employed to decipher real world engineering optimization problem ( i.e., optimal siting and sizing of distributed generation (DG) in IEEE 33-bus and practical Brazil 136-bus radial distribution system (RDS) considering different types of load models at three load levels) and proffers a real application of the suggested algorithm in the field of electrical engineering. The simulation outcomes evidence that the obtained location and size of DGs in the RDS may be feasible one and the suggested QOCHHO algorithm may be a promising optimization algorithm for the chosen engineering optimization application.

Purpose Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices. Design/methodology/approach A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms. Findings Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study. Originality/value The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.

A Wireless Sensor Network (WSN) is a group of autonomous sensors geographically distributed for environmental monitoring and tracking purposes. Since the sensors in the WSN have limited battery capacity, the energy efficiency is considered a challenging task because of redundant data transmission and inappropriate routing paths. In this research, a Quasi-Oppositional Learning (QOL)-based African Vulture Optimization Algorithm (AVOA), referred to as QAVOA, is proposed for an effective data fusion and cluster-based routing in a WSN. The QAVOA-based Back Propagation Neural Network (BPNN) is developed to optimize the weights and threshold coefficients for removing the redundant information and decreasing the amount of transmitted data over the network. Moreover, the QAVOA-based optimal Cluster Head Node (CHN) selection and route discovery are carried out for performing reliable data transmission. An elimination of redundant data during data fusion and optimum shortest path discovery using the proposed QAVOA-BPNN is used to minimize the energy usage of the nodes, which helps to increase the life expectancy. The QAVOA-BPNN is analyzed by using the energy consumption, life expectancy, throughput, End to End Delay (EED), Packet Delivery Ratio (PDR) and Packet Loss Ratio (PLR). The existing approaches such as Cross-Layer-based Harris-Hawks-Optimization (CL-HHO) and Improved Sparrow Search using Differential Evolution (ISSDE) are used to evaluate the QAVOA-BPNN method. The life expectancy of QAVOA-BPNN for 500 nodes is 4820 rounds, which is high when compared to the CL-HHO and ISSDE.