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Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness. [ABSTRACT FROM AUTHOR]

Since the past few years, several metaheuristic algorithms, inspired by the natural processes, have been introduced to solve different complex optimization problems. Studying and comparing the convergence, computational burden and statistical significance of those metaheuristics are helpful for future algorithmic development and their applications. This paper focuses on comparing the optimization performance of classical dragonfly algorithm (DA) and its seven different variants, i.e., hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder–Mead algorithm and dragonfly algorithm (INMDA) and hybridization of dragonfly algorithm and artificial bee colony (HDA) while solving 80 CEC-2021 benchmark problems. It is observed that the convergence rates of different variants of DA algorithm vary, and the corresponding computational times for such variations are also evaluated. This paper finally ranks DA and its variants according to their convergence efficiency and Friedman test. The DADE, QGDA, BMDA and DA evolve out as the most efficient algorithms for solving the considered CEC-2021 benchmark problems. [ABSTRACT FROM AUTHOR]

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maximum output power of a PV generation system. DLCI is a hybrid algorithm that integrates multiple meta-heuristic algorithms. Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms, the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum. A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration. Two structures (DLCI-I and DLCI-II) are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs. The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance. DLCI has a significant effect on the reduction in the number of power peaks caused by PSC. The PV array-based reconstruction system of DLCI-II is reduced by 4.05%, 1.88%, 1.68%, 0.99% and 3.39%, when compared to the secondary optimization algorithms. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified. [ABSTRACT FROM AUTHOR]

Introduction: The development of different optimization algorithms following the No Free-Lunch Theory has led the researchers to explore and develop new optimization algorithms which may be used to perform complicated mathematical solutions. Objectives: This paper proposes a novel Pigeon Nesting Algorithm (PNA), a single solution-based SI algorithm, which is capable of escaping the local optima due to the combination of nesting and homing behaviour of pigeon and provide accurate optimization results. Method: In order to check the validity of proposed PNA algorithm, it has been applied to the 29 benchmark functions and compared with the existing single solution and population-based meta-heuristic algorithms. In order to check the application PNA for SHM of a practical structure, its performance for damage detection of ASCE Benchmark structure using stiffness-based objective function has been compared with other well-known optimization algorithms. Results: The PNA gives good competition to the other algorithms when tested for the 29 benchmarks functions. PNA proves to be a robust algorithm for health monitoring of ASCE benchmark structure as it shows an approximate error of 1%. It has also been found that the algorithm can be used for detecting damage even in the presence of noise of 5%. Conclusion: The algorithm also outperforms other single solution-based algorithms when used for optimization of design problems. [ABSTRACT FROM AUTHOR]

Power system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows' intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA), which has been tested and evaluated by a set of well-known benchmark functions. A new update mechanism that uses the merits of the global best position to move toward the best position is proposed. This mechanism increases the convergence of the algorithm and improves its local search-ability. Twenty benchmark functions are used to evaluate the performance of the proposed ICSA. Moreover, the ICSA algorithm is compared with the conventional CSA and other meta-heuristic algorithms such as particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), gray wolf optimizer (GWO), moth-flame optimization (MFO), and sine-cosine algorithm (SCA). The experimental result shows that the proposed ICSA algorithm has produced promising results and outperformed conventional CSA and other meta-heuristic algorithms. Also, the proposed ICSA has a more robust convergence for optimizing objective functions in terms of solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) suffers from delayed convergence and stagnation in the local optimal solution, as do most meta-heuristic algorithms. This study proposes a time-based leadership particle swarm-based Salp (TPSOSA) to address the PSO's limitations. The TPSOSA is a novel search technique that addresses population diversity, an imbalance between exploitation and exploration, and the premature convergence of the PSO algorithm. Hybridization in TPSOSA is divided into two stages: The PSO hierarchy of leaders and followers is first represented as a time-varying dynamic structure. Because we need much exploration at the beginning and many exploitative steps at the end, this method raises the number of leaders while decreasing the number of follower particles linearly. In the time-varying form of the PSO (TPSOSA), unlike the PSO, the number of leaders and followers changes over time. PSO's robust search strategy is used to update the leaders' positions. Second, the SSA's powerful exploitation is utilized to update the followers' swarm population position. The purpose of tweaking the particle swarm optimizer algorithm is to aid the fundamental method in avoiding premature convergence and quickly directing the search to the most promising likely search space. The proposed TPSOSA method is tested using the CEC 2017 benchmark, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and 19 datasets (including three high-dimensional datasets and the NSL-KDD Dataset for Intrusion Detection System). In each experiment, TPSOSA is compared to various state-of-the-art metaheuristics methods. Friedman and Wilcoxon rank-sum statistical tests are also used to analyze the data. Experimental data and statistical tests show that the TPSOSA algorithm is very competitive and often superior to the algorithms used in the studies. According to the results, TPSOSA can also find an optimal feature subset that enhances classification accuracy while reducing the number of features employed. [ABSTRACT FROM AUTHOR]

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed. [ABSTRACT FROM AUTHOR]