A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

Abstract

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.

A hybrid Aquila optimizer and its K-means clustering optimization

“…In order to fully verify the effectiveness and superiority of HAO proposed in this paper, PSO (Marini and Walczak, 2015), GWO (Chrouta et al, 2021; Mirjalili et al, 2014), WOA CSO (Meng et al, 2014; Mirjalili and Lewis, 2016) and standard AO (Abualigah et al, 2021) algorithm are selected as the comparison algorithms. Twenty-one classical benchmark functions are used to test the performance of the algorithm.…”

“…Compared with traditional optimization algorithms, it is easier to implement and has been applied in many fields such as automatic control, route planning, data mining, and image processing (Blum and Groß, 2015; Li et al, 2021a; Nguyen et al, 2020). In order to solve increasingly complex optimization problems, numerous algorithms have been continuously proposed, such as particle swarm optimization (PSO; Marini and Walczak, 2015; Tian and Chen, 2021), gray wolf optimization (GWO; Chrouta et al, 2021; Mirjalili et al, 2014), and crisscross optimization (CSO; Meng et al, 2014), whale optimization algorithm (WOA; Mirjalili and Lewis, 2016; Tian et al, 2021), and so on. They are some representative swarm intelligence algorithms based on social search, which have the characteristics of fast convergence speed and few parameters; but they have the shortcomings of finding local minimum and weak global search ability (Chegini et al, 2018; Javidrad et al, 2018; Mohammed and Rashid, 2020; Nasrollahzadeh et al, 2021).…”

Image Hiding in Stochastic Geometric Moiré Gratings

“…A level-based multi-strategy learning swarm optimizer for large-scale multi-objective optimization is introduced in [34]. A multi-swarm particle swarm optimization algorithm using an adaptive factor selection strategy is described in [35].…”

Adaptive neuro-fuzzy inference system algorithm-based robust terminal sliding mode control MPPT for a photovoltaic system

“…Several MPPT approaches have been published in the literature, including Perturb and Observe (P&O) (Abdelsalam et al, 2011; Ahmed and Salam, 2015; Femia et al, 2005), Incremental Conductance (INC) (Liu et al, 2008; Sivakumar et al, 2015; Tey and Mekhilef, 2014), Fractional Short Circuit Current (FSCC) (Noguchi et al, 2002; Noh et al, 2002), Fractional Open Circuit voltage (FOCV) (Kobayashi et al, 2004), Fractional Nonlinear Synergetic Control (FNSC) (Mehiri et al, 2018), and Model Predictive Control (MPC) (Mossa et al, 2022). The use of intelligent techniques such as the Fuzzy Logic Controller (FLC) (Chuang et al, 2022; Farhat et al, 2015; Rajavel and Rathina Prabha, 2021; Rezk et al, 2019), Artificial Neural Network (ANN) (Boumaaraf et al, 2015; Rai et al, 2011), Radial Basis Function Neural Network (RBFNN) (Sitharthan et al, 2019), Adaptive Neuro-Fluent Interference System (ANFIS) (Ammar et al, 2020; Kalaiarasi et al, 2021), Genetic Algorithm (GA) (Daraban et al, 2014; Zhang et al, 2015), and Particle Size Optimization (PSO) (Chrouta et al, 2021; Duan et al, 2017; Nagarajan et al, 2022; Saad et al, 2016) are also implemented. These approaches are evaluated using different criteria, such as simplicity, convergence time, implementation details, desired sensors, cost-effectiveness ratio, and the need for correction (Ahmad et al, 2020; Saad et al, 2018).…”