The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization.

Reliability analysis of power systems has been attracting increasing attention. Regulatory agencies establish reliability standards that, if infringed, result in costly fines for the utility suppliers. A special concern pertains to the distribution networks on which most failures occur. The allocation of switches is a possible strategy to improve reliability, by allowing network reconfiguration to isolate contingencies and restore power to dark areas. This paper proposes an optimization methodology to allocate switches on radially operated distribution networks. The solution framework considers sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The approach minimizes the costs of allocation and energy not supplied, under reliability and flow capacity constraints. The solution framework is based on memetic algorithm concepts with a structured population. Case studies with a large network and real-world scenarios were used to evaluate the methodology. The results indicate that significant cost reductions can be achieved using the proposed solutions.

Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of the most popular proposals is denoted opt-aiNet (artificial immune network for optimization) and is extended here to deal with time-varying fitness functions. Additional procedures are designed to improve the overall performance and the robustness of the immune-inspired approach, giving rise to a version for dynamic optimization, denoted dopt-aiNet. Firstly, challenging benchmark problems in static multimodal optimization are considered to validate the new proposal. No parameter adjustment is necessary to adapt the algorithm according to the peculiarities of each problem. In the sequence, dynamic environments are considered, and usual evaluation indices are adopted to assess the performance of dopt-aiNet and compare with alternative solution procedures available in the literature.