A Novel Multi-objective PSO Algorithm for Constrained Optimization Problems

2006 ◽  
pp. 174-180 ◽  
Author(s):  
Jingxuan Wei ◽  
Yuping Wang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Constrained Optimization Problems ◽  
Multi Objective

scholarly journals SC: A NOVEL FUZZY CRITERION FOR SOLVING ENGINEERING AND CONSTRAINED OPTIMIZATION PROBLEMS

2017 ◽  
Vol 23 (1) ◽  
pp. 111-142
Author(s):  
Sergio G. De los Cobos Silva ◽  
Miguel A. Gutiérrez Andrade ◽  
Eric A. Rincón García ◽  
Pedro Lara Velázquez ◽  
Roman A. Mora Gutiérrez ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Breadth First Search ◽  
Constrained Optimization Problems ◽  
Engineering Problems ◽  
Convergence System ◽  
Fuzzy Convergence ◽  
Three Phases

In this paper a novel fuzzy convergence system (SC) and its fundamentals are presented. The model was implemented on a monoobjetive PSO algorithm with three phases: 1) Stabilization, 2) generation and breadth-first search, and 3) generation and depth-first. The system SC-PSO-3P was tested with several benchmark engineering problems and with several CEC2006 problems. The computing experience and comparison with previously reported results is presented. In some cases the results reported in the literature are improved.

A New Particle Swarm Algorithm for Solving Constrained Optimization Problems

2013 ◽  
Vol 734-737 ◽  
pp. 2875-2879
Author(s):  
Tie Bin Wu ◽  
Yun Cheng ◽  
Yun Lian Liu ◽  
Tao Yun Zhou ◽  
Xin Jun Li
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Constrained Optimization Problems ◽  
Particle Population ◽  
Point Set ◽  
Good Point Set ◽  
Improved Pso

Considering that the particle swarm optimization (PSO) algorithm has a tendency to get stuck at the local solutions, an improved PSO algorithm is proposed in this paper to solve constrained optimization problems. In this algorithm, the initial particle population is generated using good point set method such that the initial particles are uniformly distributed in the optimization domain. Then, during the optimization process, the particle population is divided into two sub-populations including feasible sub-population and infeasible sub-population. Finally, different crossover operations and mutation operations are applied for updating the particles in each of the two sub-populations. The effectiveness of the improved PSO algorithm is demonstrated on three benchmark functions.

scholarly journals Multi-Objective Optimization Through Pareto Minimal Correction Subsets

2018 ◽  
Author(s):  
Miguel Terra-Neves ◽  
Inês Lynce ◽  
Vasco Manquinho
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Pareto Optimal ◽  
Constrained Optimization Problems ◽  
Multi Objective ◽  
Wide Range ◽  
Constraint Set

A Minimal Correction Subset (MCS) of an unsatisfiable constraint set is a minimal subset of constraints that, if removed, makes the constraint set satisfiable. MCSs enjoy a wide range of applications, such as finding approximate solutions to constrained optimization problems. However, existing work on applying MCS enumeration to optimization problems focuses on the single-objective case. In this work, Pareto Minimal Correction Subsets (Pareto-MCSs) are proposed for approximating the Pareto-optimal solution set of multi-objective constrained optimization problems. We formalize and prove an equivalence relationship between Pareto-optimal solutions and Pareto-MCSs. Moreover, Pareto-MCSs and MCSs can be connected in such a way that existing state-of-the-art MCS enumeration algorithms can be used to enumerate Pareto-MCSs. Finally, experimental results on the multi-objective virtual machine consolidation problem show that the Pareto-MCS approach is competitive with state-of-the-art algorithms.

scholarly journals A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Biwei Tang ◽  
Zhanxia Zhu ◽  
Jianjun Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Relaxation Method ◽  
Adaptive Strategy ◽  
Pso Algorithm ◽  
Constrained Optimization Problems ◽  
Solution Quality ◽  
Swarm Optimization

This paper develops a particle swarm optimization (PSO) based framework for constrained optimization problems (COPs). Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011) algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

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