GSA is badly suffering from slow convergence rate and poor local search ability, when solving complex optimization problems. To solve this problem, a new hybrid population-based algorithm is proposed with the combination of dynamic multi swarm particle swarm optimization and gravitational search algorithm (GSADMSPSO). The proposed algorithm has divided the main population of masses into smaller sub-swarms and also stabilizing them by presenting new neighborhood strategy. Then, by adopting the global search ability of proposed algorithm, each agent (particle) improves the position and velocity. The main idea is to integrate the ability of GSA with the DMSPSO to enhance the performance of exploration and exploitation of a proposed algorithm. In order to evaluate the competences of the proposed algorithm, benchmark functions are employed. The experimental results have been confirmed a better performance of GSADMSPSO as compared to the other gravitational and PSO variants in terms of fitness rate.

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.

The utilization of both constrained and unconstrained-based optimization for solving constrained multi-objective optimization problems (CMOPs) has become prevalent among recently proposed constrained multiobjective evolutionary algorithms (CMOEAs). However, the constrained-based optimization which adopted by many CMOEAs typically gives priority to feasible solutions over infeasible ones regardless of their objective values, potentially leading to degraded performance due to the elimination of promising infeasible solutions with strong convergence and diversity. Furthermore, many existing CMOEAs have difficulty in maintaining diversity while focusing on feasibility, thereby hindering their ability to effectively address CMOPs characterized by complex feasible regions. To tackle these challenges, a constraint-Pareto dominance relationship is proposed in this paper to evaluate solutions based on both objectives and feasibility, to improve the optimization potential by reduce the elimination probability of promising infeasible solutions. A diversity enhancement strategy is also designed to enable simultaneously focus on both diversity and feasibility, thus effectively ensuring the diversity of the feasible solutions obtained. Empirical results from benchmark suites and real-world problems demonstrate that our proposed algorithm surpasses state-of-the-art CMOEAs.

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.