Cognitive Soccer League Competition algorithm for solving knapsack problems

Soccer League Competition (SLC) algorithm, is a meta-heuristic optimization technique which can strongly solve the optimization problems in discrete space. The idea of SLC is inspired from the soccer leagues and relied on the competitions between teams and players. The competition among teams to take the ownership of the top ranked locations in the league chart and the internal contest between players in each team for personal improvements are used for simulation goal and convergence of the population individuals to the global optimum. In this paper, Cognitive Soccer League Competition (CSLC) is introduced. This algorithm tries to rectify SLC for self knowledge of players of their own appearance. We try to improve the algorithm by making players have a cognition on their own performance during the league to use it for improving themselves. They use this cognition to make the results of algorithm more effective and closer to real soccer leagues. The CSLC algorithm is employed to solve knapsack problems. The experimental results on the some benchmark knapsack problems show that the CSLC algorithm is more effective, which is better than the other algorithms, in terms of search accuracy, trustiness and convergence speed.