Clonal selection algorithm for dynamic multiobjective optimization

Authors:
Ronghua Shang;Licheng Jiao;Maoguo Gong;Bin Lu
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing, Xidian University, Xi’an, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

Citing 4
Cited 4

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
A modified distance method for multicriteria optimization, using genetic algorithms

Computers and Industrial Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications

IEEE Transactions on Evolutionary Computation

Immune clonal MO algorithm for 0/1 knapsack problems

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Immune clonal MO algorithm for ZDT problems

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
A hybrid dynamic multi-objective immune optimization algorithm using prediction strategy and improved differential evolution crossover operator

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Benchmarks for dynamic multi-objective optimisation algorithms

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the clonal selection theory, a new Dynamic Multiobjective Optimization (DMO) algorithm termed as Clonal Selection Algorithm for DMO (CSADMO) is presented. The clonal selection, the nonuniform mutation and the distance method are three main operators in the algorithm. CSADMO is designed for solving continuous DMO and is tested on two test problems. The simulation results show that CSADMO outperforms another Dynamic Evolutionary Multiobjective Optimization (EMO) Algorithm: a Direction-Based Method (DBM ) in terms of finding a diverse set of solutions and in converging near the true Pareto-optimal front (POF) in each time step.