scholarly journals Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

2015 ◽  
Vol 338 ◽  
pp. 77-89 ◽  
Author(s):  
Victor H. Diaz-Ramirez ◽  
Andres Cuevas ◽  
Vitaly Kober ◽  
Leonardo Trujillo ◽  
Abdul Awwal
Keyword(s):  
Pattern Recognition ◽  
Combinatorial Optimization ◽  
Correlation Filters ◽  
Multi Objective

scholarly journals Real-Time Pattern Recognition with Adaptive Correlation Filters

10.5772/4977 ◽  
2007 ◽  
Author(s):  
Vitaly Kober ◽  
Victor H. ◽  
J. Angel ◽  
Josue Alvarez-Borrego
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Correlation Filters ◽  
Time Pattern

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.

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

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
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