Various multi–objective evolutionary algorithms (MOEAs) have obtained promising results on various numerical multi– objective optimization problems. The combination with gradi...
The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the el...
Based on the Proximate Optimality Principle in metaheuristics, a Population Based Guided Local Search (PGLS) framework for dealing with difficult combinatorial optimization problem...
The paper presents a new distributed metaheuristic algorithm in an optimal control problem related to the cooling process in the continuous casting of steel. The optimization task...
Peter Korosec, Jurij Silc, Bogdan Filipic, Erkki L...
We introduce a metaheuristic framework for combinatorial optimization. Our framework is similar to others (e.g. [1]) in that it is modular enough that important components can be i...