global optimization; controlled random search; differential evolution; self-organizing migrating algorithm; competition and cooperation in search

The aim of the paper is to study stochastic algorithms with high self-adaptation which could be used in "routine" tasks when the global optimization problem is to be solved. For such problems it is required to have algorithms that do not need a special tuning of their input parameters. The generalized controlled random search algorithm with competing heuristics and two cooperative algorithms using differential evolution and self-organizing migrating algorithm (SOMA) were compared experimentally on several test functions.

The aim of the paper is to study stochastic algorithms with high self-adaptation which could be used in "routine" tasks when the global optimization problem is to be solved. For such problems it is required to have algorithms that do not need a special tuning of their input parameters. The generalized controlled random search algorithm with competing heuristics and two cooperative algorithms using differential evolution and self-organizing migrating algorithm (SOMA) were compared experimentally on several test functions.