Integer value combinatorial optimization algorithm using multi-agents

2004 ◽  
Author(s):  
Y. Kobayashi ◽  
E. Aiyoshi
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Algorithm ◽  
Multi Agents

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.

scholarly journals A Multi-Agent Proposal for the Resolution of BIBD instances

2016 ◽  
Vol 39 (2) ◽  
pp. 267 ◽  
Author(s):  
David Rodriguez ◽  
Enrique Darghan ◽  
Julio Monroy
Keyword(s):  
Combinatorial Optimization ◽  
Experimental Design ◽  
Tabu Search ◽  
Local Search ◽  
Current Literature ◽  
Hill Climbing ◽  
Optimization Approach ◽  
Search Spaces ◽  
Multi Agent ◽  
Multi Agents

<p>The problem with designing balanced incomplete blocks (BIBD) is enclosed within the combinatorial optimization approach that has been extensively used in experimental design. The present proposal addresses thi problem by using local search techniques known as Hill Climbing, Tabu Search, and an approach based considerable sized the use of Multi-Agents, which allows the exploration of diverse areas of search spaces. Furthermore, the use of a vector vision for the consideration associated with vicinity is presented. The experimental results prove the advantage of this technique compared to other proposals that are reported in the current literature.</p>

A non-diffident combinatorial optimization algorithm

1995 ◽  
pp. 403-407
Author(s):  
Gilles Trombettoni ◽  
Bertrand Neveu ◽  
Pierre Berlandier ◽  
Maria -Cristina Riff ◽  
Mouhssine Bouzoubaa
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Algorithm
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