LACAIS: Learning Automata based Cooperative Artificial Immune System for Function Optimization

Abstract. Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions

Tracking Extrema in Dynamic Environments Using a Learning Automata-Based Immune Algorithm

Abstract. In recent years, bio-inspired algorithms have been used by researchers for solving various optimization problems increasingly. Many real world problems are mostly time varying optimization problems, which require special mechanisms for detect changes in environment and then response to them. The paper has been proposed combination of learning automata and artificial immune algorithm, in order to improve the performance of immune system algorithm in dynamic environments. In the proposed algorithm, the immune cells have equipped with a learning automaton. So they can increase diversity in response the dynamic environments. Learning automata based immune algorithm for dynamic environment has been tested in the moving parabola as a popular standard dynamic environment, and compared by several famous algorithms in dynamic environments