A new hybrid algorithm for optimization based on artificial fish swarm algorithm and cellular learning automata

In this article, a new algorithm which is obtained by hybridizing cellular learning automata and artificial fish swarm algorithm (AFSA) is proposed for optimization in continuous and static environments. In the proposed algorithm, each dimension of search space is assigned to one cell of cellular learning automata and in each cell a swarm of artificial fishes are located which have the optimization duty of that specific dimension. In fact, in the proposed algorithm for optimizing D-dimensional space, there are D one-dimensional swarms of artificial fishes that each swarm is located in one cell and they contribute with each other to optimize the D-dimensional search space. The learning automata in each cell is responsible for making diversity in artificial fishes swarm of that dimension and equivalence between global search and local search processes. The proposed algorithm with standard AFSA, Cooperative Particle swarm optimization (PSO) and global version of PSO in 10 and 30-dimensional spaces are practiced on six standard fitness functions. Experimental results show that presented method has an acceptable performance.