An improved particle swarm optimizer for mechanical design optimization problems

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

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Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406211432981 ◽

2012 ◽

Vol 226 (9) ◽

pp. 2281-2295 ◽

Cited By ~ 5

Author(s):

Mohammad Reza Farmani ◽

Jafar Roshanian ◽

Meisam Babaie ◽

Parviz M Zadeh

Keyword(s):

Particle Swarm Optimization ◽

Design Optimization ◽

Multidisciplinary Design Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Collaborative Optimization ◽

Multidisciplinary Design ◽

Fuzzy Decision ◽

Swarm Optimization ◽

Multi Objective

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

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A Particle Swarm Optimizer for Constrained Multiobjective Optimization

Research Methods ◽

10.4018/978-1-4666-7456-1.ch054 ◽

2015 ◽

pp. 1246-1276

Author(s):

Wen Fung Leong ◽

Yali Wu ◽

Gary G. Yen

Keyword(s):

Multiobjective Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Optimal Solution ◽

Constraint Handling ◽

Benchmark Problems ◽

Particle Swarm Optimizer ◽

Multiobjective Optimization Problems ◽

Feasible Solutions ◽

Constrained Multiobjective Optimization

Generally, constraint-handling techniques are designed for evolutionary algorithms to solve Constrained Multiobjective Optimization Problems (CMOPs). Most Multiojective Particle Swarm Optimization (MOPSO) designs adopt these existing constraint-handling techniques to deal with CMOPs. In this chapter, the authors present a constrained MOPSO in which the information related to particles' infeasibility and feasibility status is utilized effectively to guide the particles to search for feasible solutions and to improve the quality of the optimal solution found. The updating of personal best archive is based on the particles' Pareto ranks and their constraint violations. The infeasible global best archive is adopted to store infeasible nondominated solutions. The acceleration constants are adjusted depending on the personal bests' and selected global bests' infeasibility and feasibility statuses. The personal bests' feasibility statuses are integrated to estimate the mutation rate in the mutation procedure. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solving selected benchmark problems.

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An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

Mathematics ◽

10.3390/math7060521 ◽

2019 ◽

Vol 7 (6) ◽

pp. 521 ◽

Cited By ~ 6

Author(s):

Fanrong Kong ◽

Jianhui Jiang ◽

Yan Huang

Keyword(s):

Large Scale ◽

Optimization Problems ◽

Particle Swarm ◽

The Other ◽

Large Scale Optimization ◽

Particle Swarm Optimizer ◽

Competition Mechanism ◽

Benchmark Suite ◽

Scale Optimization ◽

The One

As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.

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An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

Mathematics ◽

10.3390/math7050414 ◽

2019 ◽

Vol 7 (5) ◽

pp. 414 ◽

Cited By ~ 3

Author(s):

Weian Guo ◽

Lei Zhu ◽

Lei Wang ◽

Qidi Wu ◽

Fanrong Kong

Keyword(s):

Large Scale ◽

Optimization Problems ◽

Particle Swarm ◽

Large Scale Optimization ◽

Particle Swarm Optimizer ◽

Fitness Evaluation ◽

Diversity Maintenance ◽

Entropy Measurement ◽

Benchmark Suite ◽

Scale Optimization

Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

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An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2018100104 ◽

2018 ◽

Vol 9 (4) ◽

pp. 71-96 ◽

Cited By ~ 3

Author(s):

Swapnil Prakash Kapse ◽

Shankar Krishnapillai

Keyword(s):

Particle Swarm Optimization ◽

Optimization Problems ◽

Mechanical Design ◽

Particle Swarm ◽

Nsga Ii ◽

Swarm Optimization ◽

Guided Search ◽

Multi Objective ◽

Search Approach ◽

Utopia Point

This article demonstrates the implementation of a novel local search approach based on Utopia point guided search, thus improving the exploration ability of multi- objective Particle Swarm Optimization. This strategy searches for best particles based on the criteria of seeking solutions closer to the Utopia point, thus improving the convergence to the Pareto-optimal front. The elite non-dominated selected particles are stored in an archive and updated at every iteration based on least crowding distance criteria. The leader is chosen among the candidates in the archive using the same guided search. From the simulation results based on many benchmark tests, the new algorithm gives better convergence and diversity when compared to existing several algorithms such as NSGA-II, CMOPSO, SMPSO, PSNS, DE+MOPSO and AMALGAM. Finally, the proposed algorithm is used to solve mechanical design based multi-objective optimization problems from the literature, where it shows the same advantages.

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On Global Convergence in Design Optimization Using the Particle Swarm Optimization Technique

Journal of Mechanical Design ◽

10.1115/1.4033727 ◽

2016 ◽

Vol 138 (8) ◽

Cited By ~ 2

Author(s):

Forrest W. Flocker ◽

Ramiro H. Bravo

Keyword(s):

Particle Swarm Optimization ◽

Global Minimum ◽

Optimization Problems ◽

Mechanical Design ◽

Optimization Technique ◽

Particle Swarm ◽

Objective Functions ◽

Convergence Criteria ◽

Swarm Optimization ◽

Particle Swarm Optimization Technique

The particle swarm optimization (PSO) method is becoming a popular optimizer within the mechanical design community because of its simplicity and ability to handle a wide variety of objective functions that characterize a proposed design. Typical examples arising in mechanical design are nonlinear objective functions with many constraints, which typically arise from the various design specifications. The method is particularly attractive to mechanical design because it can handle discontinuous functions that occur when the designer must choose from a discrete set of standard sizes. However, as in other optimizers, the method is susceptible to converging to a local rather than global minimum. In this paper, convergence criteria for the PSO method are investigated and an algorithm is proposed that gives the user a high degree of confidence in finding the global minimum. The proposed algorithm is tested against five benchmark optimization problems, and the results are used to develop specific guidelines for implementation.

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