A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem

Abstract

This paper encompasses a detailed review of state-of-the-art swarm-based algorithms with a focus on their applications along with a discussion on the merits and limitations of each algorithm. Further, a recently developed Advanced Particle Swarm Optimization (APSO) algorithm was compared with the different state-of-the-art-swarmbased algorithms through solving an electromagnetic inverse problem. Results showed that the APSO algorithm has outperformed the other algorithms. This research provides a scientific guideline for the comparison of different swarmbased algorithms and their utilization regarding specific applications.

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

“…Recent works show that swarm intelligence methods remain favorites in the optimization field [12][13][14][15][16], and their popularity has led them to be used in different application domains, such as resource planning, telecommunications, financial analysis, scheduling, space planning, energy distribution, molecular engineering, logistics, signal classification, and manufacturing, among others [17].…”

“…This type of structure is typical in many biological systems, such as insect colonies, flocks of birds, and schools of fish. The synergy between the swarm members provides each of them with advantages that they could not achieve on their own, such as protection against predators and a more reliable supply of food [14,85].…”