Investigating a hybrid simulated annealing and local search algorithm for constrained optimization
Constrained Optimization Problems (COP) often take place in many practical applications such as kinematics, chemical process optimization, power systems and so on. These problems are challenging in terms of identifying feasible solutions when constraints are non-linear and non-convex. Therefore, finding the location of the global optimum in the non-convex COP is more difficult as compared to non-convex bound-constrained global optimization problems. This paper proposes a Hybrid Simulated Annealing method (HSA), for solving the general COP. HSA has features that address both feasibility and optimality issues and here, it is supported by a local search procedure, Feasible Sequential Quadratic Programming (FSQP). We develop two versions of HSA. The first version (HSAP) incorporates penalty methods for constraint handling and the second one (HSAD) eliminates the need for imposing penalties in the objective function by tracing feasible and infeasible solution sequences independently. Numerical experiments show that the second version is more reliable in the worst case performance.
simulated annealing constrained optimization feasible sequential quadratic programming global and local search