Multi-Objective Optimization Using Artificial Immune Systems

Multi-Objective Optimization Using Artificial Immune Systems

Licheng Jiao (Xidian University, P.R. China), Maoguo Gong (Xidian University, P.R. China), Wenping Ma (Xidian University, P.R. China) and Ronghua Shang (Xidian University, P.R. China)
DOI: 10.4018/978-1-59904-498-9.ch005
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Abstract

The human immune system (HIS) is a highly evolved, parallel and distributed adaptive system. The information processing abilities of HIS provide important aspects in the field of computation. This emerging field is referring to as the Artificial Immune Systems (AIS). In recent years, AIS have received significant amount of interest from researchers and industrial sponsors. Applications of AIS include such areas as machine learning, fault diagnosis, computer security and optimization. In this chapter, after surveying the AIS for multi-objective optimization, we will describe two multi-objective optimization algorithms using AIS, the Immune Dominance Clonal Multi-objective Algorithm (IDCMA), and the Nondominated Neighbor Immune Algorithm (NNIA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. And local search only applies to the subdominant antibodies while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, Clonal Proliferation is provided to enhance local search. Using the Clonal Proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-II, SPEA, PAES, NSGA, VEGA, NPGA and HLGA in solving six well-known multi-objective function optimization problems and nine multi-objective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution. NNIA solves multi-objective optimization problems by using a nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems and three low-dimensional problems. The statistical analysis based on three performance metrics including the Coverage of two sets, the Convergence metric, and the Spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multi-objective optimization problems.

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Table of Contents
Foreword
Zbigniew Michalewicz
Acknowledgment
Lam Thu Bui, Sameer Alam
Chapter 1
Lam Thu Bui, Sameer Alam
This chapter is devoted to summarize common concepts related to multi-objective optimization (MO). An overview of “traditional” as well as CI-based... Sample PDF
An Introduction to Multi-Objective Optimization
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Chapter 2
Konstantinos E. Parsopoulos, Michael N. Vrahatis
The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems... Sample PDF
Multi-Objective Particles Swarm Optimization Approaches
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Chapter 3
Saku Kukkonen, Lampinen Jouni
Multi-objective optimization with Evolutionary Algorithms has been gaining popularity recently because its applicability in practical problems. Many... Sample PDF
Generalized Differential Evolution for Constrained Multi-Objective Optimization
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Chapter 4
Luis V. Santana-Quintero, Noel Ramírez-Santiago, Carlos A. Coello Coello
This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this... Sample PDF
Towards a More Efficient Multi-Objective Particle Swarm Optimizer
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Chapter 5
Licheng Jiao, Maoguo Gong, Wenping Ma, Ronghua Shang
The human immune system (HIS) is a highly evolved, parallel and distributed adaptive system. The information processing abilities of HIS provide... Sample PDF
Multi-Objective Optimization Using Artificial Immune Systems
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Chapter 6
Seamus M. McGovern, Surendra M. Gupta
NP-complete combinatorial problems often necessitate the use of near-optimal solution techniques including heuristics and metaheuristics. The... Sample PDF
Lexicographic Goal Programming and Assessment Tools for a Combinatorial Production Problem
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Chapter 7
Andrew Lewis, Sanaz Mostaghim, Marcus Randall
Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multi-objective... Sample PDF
Evolutionary Population Dynamics and Multi-Objective Optimisation Problems
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Chapter 8
Ramesh Rajagopalan, Chilukuri K. Mohan, Kishan G. Mehrotra, Pramod K. Varshney
Many sensor network design problems are characterized by the need to optimize multiple conflicting objectives. However, existing approaches... Sample PDF
Multi-Objective Evolutionary Algorithms for Sensor Network Design
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Chapter 9
Soo-Yong Shin, In-Hee Lee, Byoung-Tak Zhang
Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA... Sample PDF
Evolutionary Multi-Objective Optimization for DNA Sequence Design
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Chapter 10
Giuseppe Ascia, Vincenzo Catania, Alessandro G. Di Nuovo, Maurizio Palesi, Davide Patti
Multi-Objective Evolutionary Algorithms (MOEAs) have received increasing interest in industry, because they have proved to be powerful optimizers.... Sample PDF
Computational Intelligence to Speed-Up Multi-Objective Design Space Exploration of Embedded Systems
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Chapter 11
Jason Teo, Lynnie D. Neri, Minh H. Nguyen, Hussein A. Abbass
This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO)... Sample PDF
Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion
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Chapter 12
Andrea Toffolo
The research field on energy conversion systems presents a large variety of multi-objective optimization problems that can be solved taking full... Sample PDF
Evolutionary Multi-Objective Optimization in Energy Conversion Systems: From Component Detail to System Configuration
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Chapter 13
Mark P. Kleeman, Gary B. Lamont
Assignment problems are used throughout many research disciplines. Most assignment problems in the literature have focused on solving a single... Sample PDF
Evolutionary Multi-Objective Optimization for Assignment Problems
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Chapter 14
Mark P. Kleeman, Gary B. Lamont
Evolutionary methods are used in many fields to solve multi-objective optimization problems. Military problems are no exception. This chapter looks... Sample PDF
Evolutionary Multi-Objective Optimization in Military Applications
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