Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
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
Estimation of pareto sets in the mixed H2/H∞ control problem
International Journal of Systems Science
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Improved H∞ control of discrete-time fuzzy systems: a cone complementarity linearization approach
Information Sciences: an International Journal
Algorithm 860: SimpleS—an extension of Freudenthal's simplex subdivision
ACM Transactions on Mathematical Software (TOMS)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Information Sciences: an International Journal
Information Sciences: an International Journal
H/sub 2//H/sub /spl infin// filter design for systems with polytope-bounded uncertainty
IEEE Transactions on Signal Processing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Design of optimal disturbance rejection PID controllers usinggenetic algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An antibody network inspired evolutionary framework for distributed object computing
Information Sciences: an International Journal
Cost-effective base station deployment approach based on artificial immune systems
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
Robust delay-dependent stability of polytopic systems with interval time-varying delay
ACC'09 Proceedings of the 2009 conference on American Control Conference
Information Sciences: an International Journal
A new heuristic approach for non-convex optimization problems
Information Sciences: an International Journal
A novel Artificial Immune System for fault behavior detection
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
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We utilize optimization algorithms inspired by the immune system for treating the mixed H"2/H"~ control problem. Both precisely known systems and uncertain systems with polytopic uncertainties are investigated. For the latter, a novel methodology is proposed to compute the worst case norms within the polytope of matrices. This methodology consists in defining the worst case norm computation as an implicit optimization problem with a special structure. We exploit this structure of the problem for its solution. The paper presents both mono and multiobjective optimization algorithms developed from the clonal selection principle. The former is the real-coded clonal selection algorithm (RCSA) and the latter is the multiobjective clonal selection algorithm (MOCSA). The complete design process involves the combination of synthesis and analysis. The RCSA is used for analysis, through the worst case norm computation for a given provided controller. The MOCSA is used for synthesis, working on a population of candidate controllers, until providing an estimate of the Pareto set for the mixed H"2/H"~ control problem. The numerical examples illustrate the power and the validity of the proposed approach for robust control design. Moreover, our approach for worst case norm evaluation is compared with other approaches available in literature.