Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Immune procedure for optimal scheduling of complex energy systems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Artificial immune-based optimization technique for solving economic dispatch in power system
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic Polymorphic Agents Scheduling and Execution Using Artificial Immune Systems
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Learning Fuzzy Systems by a Co-Evolutionary Artificial-Immune-Based Algorithm
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
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Mathematically, an optimal power flow (OPF) is in general a non-linear, non-convex and large-scale problem with both continuous and discrete control variables. This paper approaches the OPF problem using a modified Artificial Immune System (AIS). The AIS optimization methodology uses, among others, two major immunological principles: hypermutation, which is responsible for local search, and receptor edition to explore different areas in the solution space. The proposed method enhances the original AIS by combining it with a gradient vector. This concept is used to provide valuable information during the hypermutation process, decreasing the number of generations and clones, and, consequently, speeding up the convergence process while reducing the computational time. Two applications illustrate the performance of the proposed method.