Abstract
PSO is a simple and yet powerful metaheuristic search algorithm widely used to solve various optimization problems. Nevertheless, conventional PSO tends to lose its population diversity drastically and suffer with compromised performance when encountering the optimization problems with complex fitness landscapes. Extensive studies suggest the needs of preserving high population diversity for PSO to escape from the local optima in order to solve complex optimization problems effectively. Inspired by these ideas, a hovering swarm PSO (HSPSO) is proposed in this paper, where a computationally efficient diversity preservation scheme is first introduced to divide the population of HSPSO into a main swarm and a hovering swarm. An exemplar construction scheme is subsequently proposed in the main swarm of HSPSO to generate a universal exemplar by considering the promising directional information contributed by the other non-fittest particles. The proposed universal exemplar is envisioned to suppress the negative impacts of global best particle, while remain effective to guide all particles of main swarm converging towards the promising solution regions. While hovering around the main swarm, an intelligent scheme is introduced to dynamically adjust inertia weights of all hovering swarm members to achieve proper balancing of exploration and exploitation searches at swarm levels. Extensive performance analyses are conducted by using the proposed HSPSO to solve 30 benchmark functions of CEC 2014 and five real-world engineering applications. Simulation results reveal that the HSPSO is able outperform the state-of-art optimizers when solving most tested functions due to its excellent diversity preservation capability.
Highlights
Optimization plays the essential roles in various industries and implementation of numerous real-life cases
The effectiveness of hovering swarm Particle swarm optimization (PSO) (HSPSO) in solving various optimization problems are extensively tested with 30 benchmark functions obtained from CEC 2014 at the dimensional size of D = 50
In order to evaluate the optimization performance of the proposed HSPSO, the simulation results obtained are compared with the following PSO variants, i.e., Basic PSO (BPSO) [29], modified PSO (MPSO) [34], eXpanded PSO (XPSO) [57], two-swarm learning PSO (TSLPSO) [20], fitness-based multi-role PSO (FMPSO) [61] and dynamic tournament topology PSO (DTTPSO) [50]
Summary
Optimization plays the essential roles in various industries and implementation of numerous real-life cases. These population division schemes tend to incur high computational time, especially if the frequent regrouping mechanism and large numbers of subswarm are involved [26] Another concern of multiswarm framework is the mechanisms required to achieve the good compromises of exploration and exploitation searches between different subswarms. It was observed from some existing studies that all subswarms tend to perform searching with the similar exploration and exploitation strengths due to the adoption of same learning strategy [18] This undesirable characteristic tends to restrict the adaptivity and effectiveness of these multiswarm PSO variants in handling more challenging modern optimization problems that are commonly formulated to have different types of fitness landscapes in different subregions of solution spaces.
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