Comparison of multi-modal optimization algorithms based on evolutionary algorithms

Simulated Annealing & Metaheuristic Of Deterministic Swapping [2] (SAMODS) is a hybrid local search strategy based on the MODS theory and Simulated Annealing algorithm for

THE LIGHT BEAM SEARCH APPROACH The light beam search (LBS), as described in Jaszkiewicz and Slowinski [8], combines the reference point idea and tools of multi-attribute

In the evolutionary tournament, the learning algorithms, including fictitious play and a best response to it, outperform players like Nash and survive the evolutionary process: the

A. All four in- stances are bi-objective. They are constructed by combining two benchmark single objective TSP instances. We also compared them with the original versions of MOEA/D

According to the characteristics of the above methods above to divide multi-objective evolutionary algorithm, most multi-objective evolutionary algorithms are

In this paper, we propose to combine multi-objective evolutionary algorithms with an em- bedded critical line algorithm to solve complex portfolio selection problems with

Then, it illustrates some important concepts of evolutionary multi-objective optimization algorithm, indicators of quality of metrics algorithm, the difficulties

To overcome the limitations of existing sparse model based feature selection methods, we present a novel feature selection method via directly optimizing a