PSONK: PARTICLE SWARM OPTIMIZATION WITH NEGATIVE KNOWLEDGE FOR MULTI-OBJECTIVE U-SHAPED ASSEMBLY LINES BALANCING WITH PARALLEL WORKSTATIONS

2013 ◽  
Vol 12 (01) ◽  
pp. 15-41 ◽  
Author(s):  
PARAMES CHUTIMA ◽  
SUCHADA KID-ARN
Keyword(s):  
Particle Swarm Optimization ◽  
Mixed Model ◽  
Particle Swarm ◽  
Assembly Line Balancing ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Negative Knowledge ◽  
Conflicting Objectives ◽  
Mixed Model Assembly Line

Particle swarm optimization (PSO) is a metaheuristic method inspired by the swarming behavior observed in flocks of birds. This paper proposes a novel PSO method, namely the PSO algorithm with negative knowledge (PSONK), to solve the multi-objective mixed-model assembly line balancing (MULB) problem with parallel workstations. PSONK employs the knowledge of relative positions of different particles as opposed to traditional PSO in generating new solution strings. The knowledge about poor solutions is also utilized to avoid the pairs of adjacent tasks appeared in the poor solutions from being selected as parts of the new solution strings in the next generation. The concept of Pareto optimality is employed to allow the conflicting objectives to be optimized simultaneously. Experimental results show clearly that PSONK is a promising algorithm. In addition, PSONK embedded with an appropriate local search (M-PSONK) can result in improved performances.

scholarly journals Solving a multi-objective model of job rotation minimizing the chemical exposure and cost by particle swarm optimization

2019 ◽  
Vol 11 ◽  
pp. 184797901986783 ◽  
Author(s):  
Mahdi Maleki
Keyword(s):  
Particle Swarm Optimization ◽  
Harmful Effect ◽  
Particle Swarm ◽  
Chemical Exposure ◽  
Pso Algorithm ◽  
Job Rotation ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Objective Model ◽  
Plastic Injection

Today in developing countries, perilous chemicals are widely used that have harmful effect on human resources. National and global organizations developed different approaches to control chemicals exposure and their consequences in that one of them is job rotation. Since job rotation is categorized as nondeterministic polynomial-time hardness problem, meta-heuristic methods are used to solve it such as particle swarm optimization (PSO) algorithm. In this article, because job rotation can have different and contradictory objectives, the multi-objective PSO (MOPSO) method with parallel vector evaluated PSO approach is implemented. At first, a new MOPSO algorithm is presented that solves job rotation problems to minimize chemical exposures and is finally compared with non-dominated sorting genetic algorithm. The achievement of this algorithm reduces chemicals exposure in manufacturing processes such as casting, welding, foam injection, plastic injection, which ensure workers’ health.

scholarly journals A Multi-objective Particle Swarm Optimization Based on P System Theory

2018 ◽  
Vol 232 ◽  
pp. 03039
Author(s):  
Taowei Chen ◽  
Yiming Yu ◽  
Kun Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
P System ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

Particle swarm optimization(PSO) algorithm has been widely applied in solving multi-objective optimization problems(MOPs) since it was proposed. However, PSO algorithms updated the velocity of each particle using a single search strategy, which may be difficult to obtain approximate Pareto front for complex MOPs. In this paper, inspired by the theory of P system, a multi-objective particle swarm optimization (PSO) algorithm based on the framework of membrane system(PMOPSO) is proposed to solve MOPs. According to the hierarchical structure, objects and rules of P system, the PSO approach is used in elementary membranes to execute multiple search strategy. And non-dominated sorting and crowding distance is used in skin membrane for improving speed of convergence and maintaining population diversity by evolutionary rules. Compared with other multi-objective optimization algorithm including MOPSO, dMOPSO, SMPSO, MMOPSO, MOEA/D, SPEA2, PESA2, NSGAII on a benchmark series function, the experimental results indicate that the proposed algorithm is not only feasible and effective but also have a better convergence to true Pareto front.

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy

2014 ◽  
Vol 496-500 ◽  
pp. 1895-1900
Author(s):  
Wen Wang ◽  
Wei Shen ◽  
Chao Long Ying ◽  
Xin Yi Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Search Strategy ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Experimental Results ◽  
Pareto Optimal ◽  
Optimal Search ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Convergence Performance

In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.

Application of Particle Swarm Optimization in Cylinder Helical HGearH Multi-Objective Design

2011 ◽  
Vol 474-476 ◽  
pp. 2229-2233
Author(s):  
Yuan Bin Mo ◽  
Ji Zhong Liu ◽  
Bao Lei Wang ◽  
Wei Min Wan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

Cylinder helical gGear is widely used in industry. Multi-objective optimization design of the component is often met in its different application sSituation. In this paper a novel multi-objective optimization method based on Particle Swarm Optimization (PSO) algorithm is designed for applying to solve this kind of problem. A paradigm depicted in the paper shows the algorithm is practical.

Particle Swarm Optimization to Design Ideotypes for Sustainable Fruit Production Systems

2012 ◽  
Vol 3 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Abdeslam Kadrani ◽  
Mohamed-Mahmoud Ould Sidi ◽  
Bénédicte Quilot-Turion ◽  
Michel Génard ◽  
Françoise Lescourret
Keyword(s):  
Particle Swarm Optimization ◽  
Production Systems ◽  
Particle Swarm ◽  
Brown Rot ◽  
Pso Algorithm ◽  
Fruit Production ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Linear Aggregation ◽  
Low Sensitivity

Designing peach ideotypes that satisfy the requirement of high fruit quality and low sensitivity to brown rot in a given environment was formulated as a multi-objective problem. The ‘Virtual Fruit’ model was used to perform virtual experiments. Particle swarm optimization (PSO) was interfaced to the ‘Virtual Fruit’ for solving the weighted optimization problem resulting by the linear aggregation of the criteria of the multi-objective problem. The comparison of the PSO with the Genetic Algorithm (GA) showed that the PSO method achieves better performance and outperforms the GA. The optimization results found by the PSO algorithm are considered to be satisfactory for the sustainable production systems of the peach fruit.

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

2021 ◽  
pp. 1-12
Author(s):  
Lan Zhang ◽  
Lei Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Short Term ◽  
Swarm Optimization ◽  
Load Forecast ◽  
Multi Objective ◽  
Recursive Neural Network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

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