An opposition-based algorithm for function optimization

• We introduce a comprehensive opposition (CO).
• We propose an optimization algorithm (OBA) that employs CO as its main operator.
• We use probability rules to analyze the effect of CO on the optimization performance.
• OBA converges with probability one to a global optimum.

The concept of opposition-based learning (OBL) was first introduced as a scheme for machine intelligence. In a very short period of time, some other variants of opposite numbers were proposed and opposition was applied to various research areas. In metaheuristic optimization algorithms, the main idea behind applying opposite numbers is the simultaneous consideration of a candidate solution and its corresponding opposite candidate in order to achieve a better approximation for the current solution. This paper proposes an opposition-based metaheuristic optimization algorithm (OBA) and a new and efficient opposition named comprehensive opposition (CO) as its main operator. In this paper it is mathematically proven that CO not only increases the chance of achieving better approximations for the solution but also guarantees the global convergence of OBA. The efficiency of the proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various function optimizations.

General principles of OBA. Figure optionsDownload as PowerPoint slide