Electromagnetism-like augmented lagrangian algorithm for global optimization

Abstract

This paper presents an augmented Lagrangian algorithm to solve continuous constrained global optimization problems. The algorithm approximately solves a sequence of bound constrained subproblems whose objective function penalizes equality and inequality constraints violation and depends on the Lagrange multiplier vectors and a penalty parameter. Each subproblem is solved by a population-based method that uses an electromagnetism-like mechanism to move points towards optimality. Benchmark problems are solved in a performance evaluation of the proposed augmented Lagrangian methodology. A comparison with a well-known technique is also reported.