Search

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]

Salp swarm algorithm (SSA) is a unique swarm intelligent algorithm widely used for various practical applications due to its simple framework and good optimization performance. However, like other swarm-based algorithms, SSA yields local optimal solutions and has a slow convergence rate and low solution accuracy when dealing with high-dimensional global optimization problems. Based on quadratic interpolation and a local escape operator (LEO), a salp swarm optimization algorithm (QSSALEO) is proposed to address these issues. Quadratic interpolation around the best search agent aids QSSALEO's exploitation ability and solution accuracy, whereas the local escaping operator employs random operators to escape local optima. These tactics complement one another to help SSA promote convergence performance. Furthermore, the algorithm strives for a balance of exploitation and exploration. The proposed QSSALEO method was tested using the CEC 2017 benchmark with 50 and 100 decision variables, as well as seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and its performance was compared to that of other metaheuristic algorithms and advanced algorithms, including seven salp swarm variants. The experimental results reveal that QSSALEO outperforms SSA and other population-based algorithms regarding convergence rate and solution correctness. The QSSALEO was then evaluated as a feature selection algorithm on 19 datasets (including three high-dimensional datasets). Friedman and Wilcoxon rank-sum statistical tests are also used to analyze the results. According to experimental data and statistical tests, the QSSALEO algorithm is very competitive and often superior to the algorithms employed in research. Therefore, the proposed method can also be considered a specialized large-scale global optimization optimizer whose performance surpasses state-of-the-art algorithms such as CMA-ES and SHADE. The algorithm source code is available at https://github.com/MohammedQaraad/An-Innovative-Quadratic-interpolation-Salp-Swarm. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. [ABSTRACT FROM AUTHOR]