Handling constraints in global optimization using an artificial immune system

Authors:
Nareli Cruz-Cortés;Daniel Trejo-Pérez;Carlos A. Coello Coello
Affiliations:
Evolutionary Computation Group, Depto. de Ingeniería Eléctrica, Sección de Computación, CINVESTAV-IPN, Col. San Pedro Zacatenco, México, D. F., Mexico;Evolutionary Computation Group, Depto. de Ingeniería Eléctrica, Sección de Computación, CINVESTAV-IPN, Col. San Pedro Zacatenco, México, D. F., Mexico;Evolutionary Computation Group, Depto. de Ingeniería Eléctrica, Sección de Computación, CINVESTAV-IPN, Col. San Pedro Zacatenco, México, D. F., Mexico
Venue:
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Year:
2005

Citing 9
Cited 13

New ideas in optimization

New ideas in optimization
Immune network modelling in design optimization

New ideas in optimization
Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems

Evolutionary Computation
Immune inspired somatic contiguous hypermutation for function optimisation

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Stochastic ranking for constrained evolutionary optimization

IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization

IEEE Transactions on Evolutionary Computation

Application areas of AIS: The past, the present and the future

Applied Soft Computing
Optimization of Fuzzy Membership Function Using Clonal Selection

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Artificial immune based hybrid GA for QoS based multicast routing in large scale networks (AISMR)

Computer Communications
Optimization of multiple input single output fuzzy membership functions using clonal selection algorithm

ACS'08 Proceedings of the 8th conference on Applied computer scince
Overview of artificial immune systems for multi-objective optimization

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A novel model of artificial immune system for solving constrained optimization problems with dynamic tolerance factor

MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A cultural immune system for economic load dispatch with non-smooth cost functions

ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
A Survey of artificial immune applications

Artificial Intelligence Review
Optimization of multiple input-output fuzzy membership functions using clonal selection algorithm

Expert Systems with Applications: An International Journal
A review of clonal selection algorithm and its applications

Artificial Intelligence Review
Design of fixed and ladder mutation factor-based clonal selection algorithm for solving unimodal and multimodal functions

Applied Computational Intelligence and Soft Computing
Co-evolutionary immuno-particle swarm optimization with penetrated hyper-mutation for distributed inventory replenishment

Engineering Applications of Artificial Intelligence
An adaptive single-point algorithm for global numerical optimization

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a study of the use of an artificial immune system (CLONALG) for solving constrained global optimization problems. As part of this study, we evaluate the performance of the algorithm both with binary encoding and with real-numbers encoding. Additionally, we also evaluate the impact of the mutation operator in the performance of the approach by comparing Cauchy and Gaussian mutations. Finally, we propose a new mutation operator which significantly improves the performance of CLONALG in constrained optimization.