Search

• A general optimization algorithm for a cooperative hyper-heuristics is described. • Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization are included • A Master processor communicates with all agents and ranks solution candidates • Both on-line parameter retuning and parallel processing speed up the search. • It was effective to optimise PSE models within acceptable computational times. This paper presents the general framework of a parallel cooperative hyper-heuristic optimizer (PCHO) to solve systems of nonlinear algebraic equations with equality and inequality constraints. The algorithm comprises the classical metaheuristics called Genetic Algorithms, Simulated Annealing and Particle Swarm Optimization, whose parameters are adaptively chosen during the executions. A Master-Worker architecture was designed and implemented, where the Master processor ranks the solution candidates informed by the metaheuristics and immediately communicates the most promising candidate to update all Workers. Algorithmic performance was tested with general models, most of them corresponding to PSE process systems. The results confirmed the efficiency of the proposed approach since both online parameter retuning and parallel processing sped up the search. [Display omitted] [ABSTRACT FROM AUTHOR]