Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm - L. Falahiazar; H. Shah-Hosseini (Connection Science)
A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing Multi-Objective Genetic Algorithm (Spacing-MOGA). The innovation of the proposed Spacing-MOGA lies in a new survival selection algorithm called Spacing Distance. This research eliminates some of the disadvantages of other algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGAII). The proposed Spacing-MOGA is applied to five test benchmark functions and also to the design of I-Beam. Then, the results are compared with other algorithms such as NSGAII, Adaptive Weighted Particle Swarm Optimisation (AWPSO), and Non-dominated Sorting Particle Swarm Optimiser (NSPSO) based on the test metrics: Hypervolume, Spacing, Spread, and Generational Distance. Furthermore, for further demonstration of the ability of the proposed Spacing-MOGA, the experimental results are evaluated by the t-test.