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

Abstract

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.

Adaptive cooperative particle swarm optimizer

“…In [56] a new method of AIS utilizing cooperative concept of learning automata is proposed. Firefly algorithm [54] is a new evolutionary optimization algorithm which works based on the flashing characteristics of the fireflies.…”

Solving maximum clique problem in stochastic graphs using learning automata

“…A learning automaton [10][11][12][13] is an adaptive decision making unit that improves its performance by learning how choose the optimal action from a finite set of allowed actions through repeated interactions with a random environment. The action is chosen at random based on a probability distribution kept over the action-set and at each instant the given action is served as the input to the random environment.…”

A robust heuristic algorithm for Cooperative Particle Swarm Optimizer: A Learning Automata approach

“…Evolutionary Algorithms (EA) [9], [10]. Also a new hybrid method of optimization which called PS�-LA [11][12][13] has been emerged.…”

“…Parameter adaption [9], [10] is one of the most difficult tasks in evolutionary algorithms. As there are multiple parameters in PSO, it needs a mechanism to tune them during the evaluation of the population.…”

“…Observing the learning automaton's [9], [10] behavior during the evolution process, three different kind of learning algorithms are placed in CPSOLA algorithm. The detailed configurations of the reward and penalty parameters are mentioned in Table II.…”