Integer cat swarm optimization algorithm for multiobjective integer problems

In the literature, several variants of cat swarm optimization (CSO) algorithm are reported. However, CSO for integer multiobjective optimization problems (MOPs) has not yet been investigated. Owing to the frequent occurrence of integer MOPs and their importance in practical design problems, in this...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 24; no. 3; pp. 1927 - 1955
Main Authors: Ali Murtza, Shahid, Ahmad, Ayaz, Shafique, Jawad
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Business metrics
Cats
Computational Intelligence
Control
Design optimization
Engineering
Genetic algorithms
Integer programming
Integers
Investigations
Knapsack problem
Lagrange multiplier
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Multiple objective analysis
Optimization algorithms
Parameter sensitivity
Pareto optimization
Performance evaluation
Performance measurement
Robotics
Variables
Velocity
Cat swarm optimization
Swarm intelligence
Multiobjective optimization
Integer optimization
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:In the literature, several variants of cat swarm optimization (CSO) algorithm are reported. However, CSO for integer multiobjective optimization problems (MOPs) has not yet been investigated. Owing to the frequent occurrence of integer MOPs and their importance in practical design problems, in this work, we investigate a new CSO approach for solving purely integer MOPs. This new approach named as multiobjective integer cat swarm optimization (MO-ICSO) algorithm incorporates the modified version of the CSO algorithm for MOPs. This approach is comprised of the concepts of rounding the floating points to the nearest integer numbers and the probabilistic updating (PU) technique. It uses the idea of Pareto dominance for finding the non-dominated solutions and an external archive for storing these solutions. We demonstrate the power of this new approach via its quantitative analysis and sensitivity test of its several parameters using different performance metrics performed over multiobjective multidimensional knapsack problem and several standard test functions. The simulation results argue that the proposed MO-ICSO approach can be a better candidate for solving the integer MOPs.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-04023-1