Knowledge-based solution to dynamic optimization problems using cultural algorithms
Knowledge-based solution to dynamic optimization problems using cultural algorithms
Optimization with constraints using a cultured differential evolution approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 2006 ACM symposium on Applied computing
Fundamentals of Natural Computing (Chapman & Hall/Crc Computer and Information Sciences)
Fundamentals of Natural Computing (Chapman & Hall/Crc Computer and Information Sciences)
Knowledge Integration On-The-Fly in Swarm Intelligent Systems
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Handling constraints in global optimization using an artificial immune system
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A review of clonal selection algorithm and its applications
Artificial Intelligence Review
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This paper presents a novel and efficient method for solving economic load dispatch problems with non-smooth cost functions, by combining an Artificial Immune Systems with Cultural Algorithms. The proposed method, called Cultural Immune System, uses a real coded AIS that is derived from the clonal selection principle with a pure aging operator and hypermutation operators based on Gaussian and Cauchy mutations that are guided by four knowledge sources stored in the belief space of a Cultural Algorithm. The Cultural Immune System has a local search stage that is based on a quasi-simplex technique and several points of self-adaptation. Three test systems with thermal units whose fuel cost function takes into account valve-point loading effects are used to validate the proposed method. These test systems constitute complex constrained optimization problems. Firstly, Cultural Immune System is compared with his noncultural counterpart (the same AIS without knowledge sources guiding the hypermutation operators). After that both immune-based methods are compared with state-of-the-art algorithms. The results show that the Cultural Immune System is capable of outperforming other state-of-the-art algorithms in solving load dispatch problems with the valve-point effect.