Victoria S. Aragón

An immune algorithm with power redistribution for solving economic dispatch problems

In this paper, we present an algorithm inspired on the T-Cell model of the immune system (i.e., an artificial immune system), which is used to solve economic dispatch problems. The proposed approach is called IA_EDP, which stands for Immune Algorithm for Economic Dispatch Problem, and it uses two versions of a redistribution power operator which tries to keep feasible the solutions that it finds. The proposed approach is validated using eight problems taken from the specialized literature. Our results are compared with respect to those obtained by several other approaches. We also perform some statistical analysis in order to determine the sensitivity of our proposed approach to its parameters.

A T-cell algorithm for solving dynamic optimization problems

In this paper, a metaheuristic inspired on the T-Cell model of the immune system (i.e., an artificial immune system) is introduced. The proposed approach (called DTC, for Dynamic T-Cell) is used to solve dynamic optimization problems, and is validated using test problems taken from the specialized literature on dynamic optimization. Results are compared with respect to artificial immune approaches representative of the state-of-the-art in the area. Some statistical analyses are also performed, in order to determine the sensitivity of the proposed approach to its parameters.

Artificial Immune System for Solving Dynamic Constrained Optimization Problems

In this chapter, we analyze the behavior of an adaptive immune system when solving dynamic constrained optimization problems (DCOPs). Our proposed approach is called Dynamic Constrained T-Cell (DCTC) and it is an adaptation of an existing algorithm, which was originally designed to solve static constrained problems. Here, this approach is extended to deal with problems which change over time and whose solutions are subject to constraints. Our proposed DCTC is validated with eleven dynamic constrained problems which involve the following scenarios: dynamic objective function with static constraints, static objective function with dynamic constraints, and dynamic objective function with dynamic constraints. The performance of the proposed approach is compared with respect to that of another algorithm that was originally designed to solve static constrained problems (SMES) and which is adapted here to solve DCOPs. Besides, the performance of our proposed DCTC is compared with respect to those of two approaches which have been used to solve dynamic constrained optimization problems (RIGA and dRepairRIGA). Some statistical analysis is performed in order to get some insights into the effect that the dynamic features of the problems have on the behavior of the proposed algorithm.

A novel model of artificial immune system for solving constrained optimization problems with dynamic tolerance factor

In this paper, we present a novel model of an artificial immune system (AIS), based on the process that suffers the T-Cell. The proposed model is used for solving constrained (numerical) optimization problems. The model operates on three populations: Virgins, Effectors and Memory. Each of them has a different role. Also, the model dynamically adapts the tolerance factor in order to improve the exploration capabilities of the algorithm. We also develop a new mutation operator which incorporates knowledge of the problem. We validate our proposed approach with a set of test functions taken from the specialized literature and we compare our results with respect to Stochastic Ranking (which is an approach representative of the state-of-the-art in the area) and with respect to an AIS previously proposed.