A Particle Swarm Optimizer for Constrained Multiobjective Optimization

Feasible Solutions ◽

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.

A Multiobjective Particle Swarm Optimizer for Constrained Optimization

Recent Algorithms and Applications in Swarm Intelligence Research ◽

10.4018/978-1-4666-2479-5.ch005 ◽

2013 ◽

pp. 71-95

Author(s):

Gary G. Yen ◽

Wen-Fung Leong

Keyword(s):

Optimization Problems ◽

Particle Swarm ◽

Optimal Solution ◽

Constraint Handling ◽

Benchmark Problems ◽

Feasible Solutions ◽

Constraint handling techniques are mainly 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 the proposed constrained MOPSO, information related to particles’ infeasibility and feasibility status is utilized effectively to guide the particles to search for feasible solutions and improve the quality of the optimal solution. This information is incorporated into the four main procedures of a standard MOPSO algorithm. The involved procedures include the updating of personal best archive based on the particles’ Pareto ranks and their constraint violation values; the adoption of infeasible global best archives to store infeasible nondominated solutions; the adjustment of acceleration constants that depend on the personal bests’ and selected global best’s infeasibility and feasibility status; and the integration of personal bests’ feasibility status to estimate the mutation rate in the mutation procedure. Simulation to investigate the proposed constrained MOPSO in solving the selected benchmark problems is conducted. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solving most of the selected benchmark problems.

A Multiobjective Particle Swarm Optimizer for Constrained Optimization

International Journal of Swarm Intelligence Research ◽

10.4018/jsir.2011010101 ◽

2011 ◽

Vol 2 (1) ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Gary G. Yen ◽

Wen-Fung Leong

Keyword(s):

Optimization Problems ◽

Particle Swarm ◽

Optimal Solution ◽

Constraint Handling ◽

Benchmark Problems ◽

Feasible Solutions ◽

Constraint handling techniques are mainly 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 the proposed constrained MOPSO, information related to particles’ infeasibility and feasibility status is utilized effectively to guide the particles to search for feasible solutions and improve the quality of the optimal solution. This information is incorporated into the four main procedures of a standard MOPSO algorithm. The involved procedures include the updating of personal best archive based on the particles’ Pareto ranks and their constraint violation values; the adoption of infeasible global best archives to store infeasible nondominated solutions; the adjustment of acceleration constants that depend on the personal bests’ and selected global best’s infeasibility and feasibility status; and the integration of personal bests’ feasibility status to estimate the mutation rate in the mutation procedure. Simulation to investigate the proposed constrained MOPSO in solving the selected benchmark problems is conducted. The simulation results indicate that the proposed constrained MOPSO is highly competitive in solving most of the selected benchmark problems.

Adaptive and Accelerated Exploration Particle Swarm Optimizer (AAEPSO) for Solving Constrained Multiobjective Optimization Problems

Swarm, Evolutionary, and Memetic Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-17563-3_19 ◽

2010 ◽

pp. 155-162

Author(s):

Layak Ali ◽

Samrat L. Sabat ◽

Siba K. Udgata

Keyword(s):

Multiobjective Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

An improved proximal method with quasi-distance for nonconvex multiobjective optimization problem

Journal of Applied Analysis ◽

10.1515/jaa-2021-2074 ◽

2022 ◽

Vol 0 (0) ◽

Author(s):

Fouzia Amir ◽

Ali Farajzadeh ◽

Jehad Alzabut

Keyword(s):

Multiobjective Optimization ◽

Optimization Problems ◽

Optimal Solution ◽

Convergence Result ◽

Proximal Method ◽

Pareto Solution ◽

Locally Lipschitz ◽

The Cost ◽

Abstract Multiobjective optimization is the optimization with several conflicting objective functions. However, it is generally tough to find an optimal solution that satisfies all objectives from a mathematical frame of reference. The main objective of this article is to present an improved proximal method involving quasi-distance for constrained multiobjective optimization problems under the locally Lipschitz condition of the cost function. An instigation to study the proximal method with quasi distances is due to its widespread applications of the quasi distances in computer theory. To study the convergence result, Fritz John’s necessary optimality condition for weak Pareto solution is used. The suitable conditions to guarantee that the cluster points of the generated sequences are Pareto–Clarke critical points are provided.