An Improved Multiobjective Particle Swarm Optimization Algorithm Using Minimum Distance of Point to Line

Abstract

In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. In this paper, a new method is introduced for selecting the global best particle, which is minimum distance of point to line multiobjective particle swarm optimization (MDPL-MOPSO). Using the basic concept of minimum distance of point to line and objective, the global best particle among archive members can be selected. Different test functions were used to test and compare MDPL-MOPSO with CD-MOPSO. The result shows that the convergence and diversity of MDPL-MOPSO are relatively better than CD-MOPSO. Finally, the proposed multiobjective particle swarm optimization algorithm is used for the Pareto optimal design of a five-degree-of-freedom vehicle vibration model, which resulted in numerous effective trade-offs among conflicting objectives, including seat acceleration, front tire velocity, rear tire velocity, relative displacement between sprung mass and front tire, and relative displacement between sprung mass and rear tire. The superiority of this work is demonstrated by comparing the obtained results with the literature.

A methodology for scheduling within‐day roadway work zones using deep neural networks and active learning

“…For purposes of comparing the performance of the implemented algorithm with benchmark algorithms, the road project scheduling problem is also solved using the improved versions of two metaheuristics, namely, NSGA‐II (Mohapatra et al., 2015) and MOPSO (Fan et al., 2017). To provide a fair comparison, the same initial population set, crossover, and mutation operators are used for both the improved NSGA‐II and DN2EA2L algorithms.…”

Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

“…Some methods have developed new criteria for selecting leaders from the repository of non-dominated solutions. For example, a study by [ 38 ] developed an improved MOO variant model and provided an algorithm for selecting leaders from sets of non-dominated solutions using a geometrical approach. The approach selected points that had the least distance from the line fitted model for the set of non-dominated solutions.…”

A novel multi-objective quantum particle swarm algorithm for suspension optimization

“…The results from Al-Baity (2015) show that the QPSO is a valid tool to multi-objective problems, and can lead to better results in some benchmark functions than other famous algorithms such as NSGA-II (Deb et al 2002), PAEAS, and SPEA2. Recently, Fan et al (2017) present a paper where a new MOPSO is proposed based on the minimum distance of point-to-line called MDPL-MOPSO. The algorithm is tested with the well-known ZDT multi-objective functions and with a half-car suspension model with multi-objective functions such as acceleration, suspension stroke, and velocity.…”

“…The five degrees of freedom half-car suspension model is used in the first case of the present work and was previously analyzed by Fan et al (2017), Nariman-Zadeh et al (2010, Mahmoodabadi et al (2013), and Boonlong (2013) using different multi-objective optimization algorithms. The use of the Multi-objective Uniform-diversity Genetic Algorithm (MUGA) with a diversity mechanism called the ε-elimination algorithm is used by Nariman-Zadeh et al (2010) to optimize four different pairs of objective functions and a set of five conflicting objective functions at the same time in the 5 DOF half-car problem.…”