Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Application areas of AIS: The past, the present and the future
Applied Soft Computing
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
An novel artificial immune systems multi-objective optimization algorithm for 0/1 knapsack problems
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Immune multiobjective optimization algorithm for unsupervised feature selection
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Clonal selection with immune dominance and anergy based multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Effects of Similarity-Based Selection on WBMOIA: A Weight-Based Multiobjective Immune Algorithm
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Coupling of immune algorithms and game theory in multiobjective optimization
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
A quantum immune algorithm for multiobjective parallel machine scheduling
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
A quantum-inspired artificial immune system for multiobjective 0-1 knapsack problems
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
A novel micro-population immune multiobjective optimization algorithm
Computers and Operations Research
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This study presents a novel weight-based multiobjective artificial immune system (WBMOAIS) based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. The proposed algorithm follows the elementary structure of opt-aiNET, but has the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function. The fitness assignment has a much lower computational complexity than that based on Pareto ranking, (2) the individuals of the population are chosen from the memory, which is a set of elite solutions, and a local search procedure is utilized to facilitate the exploitation of the search space, and (3) in addition to the clonal suppression algorithm similar to that used in opt-aiNET, a new truncation algorithm with similar individuals (TASI) is presented in order to eliminate similar individuals in memory and obtain a well-distributed spread of non-dominated solutions. The proposed algorithm, WBMOAIS, is compared with the vector immune algorithm (VIS) and the elitist non-dominated sorting genetic system (NSGA-II) that are representative of the state-of-the-art in multiobjective optimization metaheuristics. Simulation results on seven standard problems (ZDT6, SCH2, DEB, KUR, POL, FON, and VNT) show WBMOAIS outperforms VIS and NSGA-II and can become a valid alternative to standard algorithms for solving multiobjective optimization problems.