Dual Level Searching Approach for Solving Multi Objective Optimisation Problems using Hybrid Particle Swarm Optimisation and Bats Echolocation-inspired Algorithms

  • Nafrizuan Mat Yahya Universiti Malaysia Pahang
  • Ahmad Razlan Yusoff Universiti Malaysia Pahang
  • Azlyna Senawi Universiti Malaysia Pahang
  • M. Osman Tokhi London South Bank University
Keywords: Modified adaptive bats sonar algorithm, bats echolocation, particle swarm optimisation, multi objective optimisation

Abstract

A dual level searching approach for multi objective optimisation problems using particle swarm optimisation and modified adaptive bats sonar algorithm is presented. The concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is integrated with the established particle swarm optimisation algorithm. The proposed algorithm incorporates advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handle the complexity of multi objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi objective optimisation benchmark test functions and problem. The acquired results show that the proposed algorithm perform well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving of multi objective optimisation problems.

Published
2019-01-31
How to Cite
[1]
N. Mat Yahya, A. R. Yusoff, A. Senawi, and M. O. Tokhi, “Dual Level Searching Approach for Solving Multi Objective Optimisation Problems using Hybrid Particle Swarm Optimisation and Bats Echolocation-inspired Algorithms”, Mekatronika, vol. 1, no. 1, pp. 45-57, Jan. 2019.
Section
Original Article