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 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.

scholarly journals AN ADAPTIVE CONSTRAINT HANDLING TECHNIQUE FOR PARTICLE SWARM IN CONSTRAINED OPTIMIZATION PROBLEMS

2016 ◽  
Vol 8 (1) ◽  
pp. 39 ◽  
Author(s):  
Érica Da Costa Reis Carvalho ◽  
José Pedro Gonçalves Carvalho ◽  
Heder Soares Bernardino ◽  
Patrícia Habib Hallak ◽  
Afonso Celso de Castro Lemonge
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Test Problems ◽  
Constrained Optimization Problems ◽  
Comparison Results ◽  
Engineering Problems ◽  
Adaptive Penalty ◽  
Handling Technique ◽  
Adaptive Penalty Method

Nature inspired meta-heuristics are largely used to solve optimization problems. However, these techniques should be adapted when solving constrained optimization problems, which are common in real world situations. Here an adaptive penalty approach (called Adaptive Penalty Method, APM) is combined with a particle swarm optimization (PSO) technique to solve constrained optimization problems. This approach is analyzed using a benchmark of test-problems and 5 mechanical engineering problems. Moreover, three variants of APM are considered in the computational experiments. Comparison results show that the proposed algorithm obtains a promising performance on the majority of the test problems

scholarly journals An Efficient Algorithm for Unconstrained Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Sergio Gerardo de-los-Cobos-Silva ◽  
Miguel Ángel Gutiérrez-Andrade ◽  
Roman Anselmo Mora-Gutiérrez ◽  
Pedro Lara-Velázquez ◽  
Eric Alfredo Rincón-García ◽  
...  
Keyword(s):  
Unconstrained Optimization ◽  
Efficient Algorithm ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Numerical Results ◽  
Breadth First Search ◽  
Depth First Search ◽  
Unconstrained Optimization Problems ◽  
Three Phases

This paper presents an original and efficient PSO algorithm, which is divided into three phases: (1) stabilization, (2) breadth-first search, and (3) depth-first search. The proposed algorithm, called PSO-3P, was tested with 47 benchmark continuous unconstrained optimization problems, on a total of 82 instances. The numerical results show that the proposed algorithm is able to reach the global optimum. This work mainly focuses on unconstrained optimization problems from 2 to 1,000 variables.

scholarly journals A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Zhijun Luo ◽  
Lirong Wang
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Linear Equations ◽  
Coefficient Matrix ◽  
Descent Direction ◽  
Constrained Optimization Problems ◽  
Inequality Constrained Optimization ◽  
Globally Convergent ◽  
Variable Distribution ◽  
Systems Of Linear Equations

A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.

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