Abstract
In recent years, particle swarm optimization (PSO) has been extensively applied in various optimization problems because of its structural and implementation simplicity. However, the PSO can sometimes find local optima or exhibit slow convergence speed when solving complex multimodal problems. To address these issues, an improved PSO scheme called fusion global-local-topology particle swarm optimization (FGLT-PSO) is proposed in this study. The algorithm employs both global and local topologies in PSO to jump out of the local optima. FGLT-PSO is evaluated using twenty (20) unimodal and multimodal nonlinear benchmark functions and its performance is compared with several well-known PSO algorithms. The experimental results showed that the proposed method improves the performance of PSO algorithm in terms of solution accuracy and convergence speed.
Highlights
particle swarm optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart [1] in 1995
Other researchers presented the several variants of PSO algorithms such as dynamic multiswarm PSO (DMS-PSO) [12], comprehensive learning PSO (CLPSO) [13], median-oriented particle swarm optimization (MPSO) [14], centripetal accelerated particle swarm optimization (CAPSO) [15], quadratic interpolation PSO (QIPSO) [16], quantum-behaved particle swarm optimization (QPSO) [17], and adaptive particle swarm optimization (APSO) [18]
The FGLT-PSO algorithm is compared with some well-known PSO algorithms
Summary
PSO is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart [1] in 1995. A number of variant PSO algorithms have been proposed in the literature to avoid the local optima and to find the best solution promptly. Based on Pi⃗ and P⃗g, the velocity and position of the ith particle are computed using (3) and (4) as follows: Vid (t + 1) = w × Vid (t) + C1 × rand1 × (pid (t) − xid (t)) + C2. A variant of PSO algorithm based on orthogonal learning strategy (OLPSO) [32] was introduced to guide particles for discovering useful information from their personal best positions and from their neighborhood’s best position in order to fly in better directions. The algorithm called CSPSO finds new solutions in the neighborhoods of the previous best positions in order to escape from local optima in multimodal functions. Beheshti et al proposed median-oriented particle swarm optimization (MPSO) [14] and centripetal accelerated particle swarm optimization (CAPSO) [15] based on Newton’s laws of motion to accelerate the learning and convergence of optimization problems
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