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Based on clonal selection principle, this paper proposes an immune-inspired evolution strategy (IIES) for constrained optimization problems with two improvements. Firstly, in order to enhance global search capability, more clones are produced by individuals that have far-off nearest neighbors in the less-crowed regions. On the other hand, immune update mechanism is proposed to replace the worst individuals in clone population with the best individuals stored in immune memory in every generation. Therefore, search direction can always focus on the fittest individuals. These proposals are able to avoid being trapped in local optimal regions and remarkably enhance global search capability. In order to examine the optimization performance of IIES, 13 well-known benchmark test functions are used. When comparing with various state-of-the-arts and recently proposed competent algorithms, simulation results show that IIES performs better or comparably in most cases.

A comprehensive model of the immune classifier system is presented where the affinity between the epitope- paratope pairs is defined in terms of structural matching and compatibility with respect to important physicochemical and geometrical parameters. Effects of medium interactions (energy constraints) on adaptive maturation of affinity and clonal selection are also simulated in terms of certain cost functions. Results of implementation on some real data are presented focussing on the nonlinear effects of the probability and strength of binding interactions and the metadynamics of variation in system parameters. Importance and scope of the approach in understanding the complexity of the associated nonlinear stochastic (bio)system is highlighted.

In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, i.e. dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM) operator. The main notion of these approaches is to perform more coarse-grained search at initial stage in order to speed up the convergence toward the Pareto-optimal front. Once the solutions are getting close to the Pareto-optimal front, more fine-grained search is performed in order to reduce the gaps between the solutions and the Pareto-optimal front. Based on this purpose, a cooling schedule is adopted in these approaches, reducing the parameters gradually to a minimal threshold, the aim of which is to keep a desirable balance between fine-grained search and coarse-grained search. By this means, the exploratory capabilities of NCMO are enhanced. When compared with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that NCMO has remarkable performance.

Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

This paper deals with intelligent tuning by hybrid system composed of genetic algorithm and clonal selection (CS). This paper uses gain margin (GM) and phase margin (PM) to define the tuning margin of control system for optimal tuning. Up to this time, many intelligent algorithms have been tried in order to improve the intelligent controller performance for industrial areas such as power plant, motor control, chemical control, and so on. However, in the actual plant, they are many limitations in tuning approaching because of computing speed. Therefore, it is difficult to apply for getting optimum value in tuning. In this paper, novel computing scheme has been done by hybrid CS algorithm and genetic algorithm, GM, and PM to obtain an advanced value and effective approach for optimal parameters.

Inspired by the foraging behavior of E. coli bacteria, bacterial foraging optimization (BFO) has emerged as a powerful technique for solving optimization problems. However, BFO shows poor performance on complex and high-dimensional optimization problems. In order to improve the performance of BFO, a new dynamic bacterial foraging optimization based on clonal selection (DBFO-CS) is proposed. Instead of fixed step size in the chemotaxis operator, a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process, which can improve convergence speed. Furthermore, reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected, which can enhance convergence precision. Then, a set of benchmark functions have been used to test the proposed algorithm. The results show that DBFO-CS offers significant improvements than BFO on convergence, accuracy and robustness. A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented. Results show that the proposed algorithm can efficiently approximate the systems.