Taboo search: an approach to the multiple minima problem.

Area of Science:

• Numerical Analysis
• Computational Mathematics
• Optimization Theory

Background:

• The multiple minima problem poses a significant challenge in continuous function optimization.
• Local minima often trap traditional optimization algorithms, preventing the discovery of global optima.

Purpose of the Study:

• To introduce a novel, derivative-free algorithm for solving the multiple minima problem in continuous functions.
• To demonstrate the algorithm's ability to avoid local minima and achieve near-optimal solutions.

Main Methods:

• The method is based on Glover's taboo search algorithm, adapted for continuous functions.
• Model calculations were used to demonstrate the algorithm's performance.

Main Results:

• The algorithm successfully avoids entrapment in local minima.
• Model calculations confirm the procedure's ability to find near-optimal solutions.

Conclusions:

• The proposed method offers a generally applicable, simple, and derivative-free approach to global optimization.
• This technique provides a robust solution for problems characterized by multiple local minima.