Core-guided method for constraint-based multi-objective combinatorial optimization

2020 ◽  
Author(s):  
Naiyu Tian ◽  
Dantong Ouyang ◽  
Yiyuan Wang ◽  
Yimou Hou ◽  
Liming Zhang
Keyword(s):  
Combinatorial Optimization ◽  
Multi Objective

A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

2012 ◽  
pp. 1-16
Author(s):  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung Bae Cho
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Discrete Values ◽  
Continuous Domain

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

QMAEA: A quantum multi-agent evolutionary algorithm for multi-objective combinatorial optimization

SIMULATION ◽  
2013 ◽  
Vol 90 (2) ◽  
pp. 182-204 ◽  
Author(s):  
F Tao ◽  
Y J Laili ◽  
L Zhang ◽  
Z H Zhang ◽  
AY C Nee
Keyword(s):  
Combinatorial Optimization ◽  
Evolutionary Algorithm ◽  
Multi Objective ◽  
Multi Agent

Runtime Analysis of an Evolutionary Algorithm for Stochastic Multi-Objective Combinatorial Optimization

2012 ◽  
Vol 20 (3) ◽  
pp. 395-421 ◽  
Author(s):  
Walter J. Gutjahr
Keyword(s):  
Combinatorial Optimization ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Fast Convergence ◽  
Runtime Analysis ◽  
Multi Objective ◽  
Complexity Results ◽  
Starting Point

For stochastic multi-objective combinatorial optimization (SMOCO) problems, the adaptive Pareto sampling (APS) framework has been proposed, which is based on sampling and on the solution of deterministic multi-objective subproblems. We show that when plugging in the well-known simple evolutionary multi-objective optimizer (SEMO) as a subprocedure into APS, ε-dominance has to be used to achieve fast convergence to the Pareto front. Two general theorems are presented indicating how runtime complexity results for APS can be derived from corresponding results for SEMO. This may be a starting point for the runtime analysis of evolutionary SMOCO algorithms.

scholarly journals Bi-level multi-objective combinatorial optimization using reference approximation of the lower level reaction

2020 ◽  
Vol 176 ◽  
pp. 2098-2107
Author(s):  
Malek Abbassi ◽  
Abir Chaabani ◽  
Lamjed Ben Said ◽  
Nabil Absi
Keyword(s):  
Combinatorial Optimization ◽  
Multi Objective
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