New Hybrid Evolutionary Algorithm for Solving Multi-Objective Optimization Problems

Multi-objective evolutionary algorithms are used in a wide range of fields to solve the issues of optimization, which require several conflicting objectives to be considered together. Basic evolutionary algorithm algorithms have several drawbacks,such as lack of a good criterion for termination, and lack of evidence of good convergence. A multi-objective hybrid evolutionary algorithm is often used to overcome these defects.

New Hybrid Evolutionary Algorithm for the Multi-Objective Traveling Salesman Problem (moTSP)

In the Multi-objective Traveling Salesman Problem (moTSP) simultaneous optimization of more than one objective functions is required. This paper proposes hybrid algorithm to solve the multiobjectives Traveling Salesman problem through the integration of the ant colony optimization algorithm with the Genetic algorithm.

Multi-Objective Optimization Algorithms to Solve Molecular Docking

Molecular docking is a hard optimization problem that has been tackled in the past, demonstrating new and challenging results when looking for one objective . However, only a few papers can be found in the literature that deal with this problem bymeans of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this research, we use and compare, a set of representative multi-objective optimization algorithms. The approach followed is focused on optimizing the inter-molecular and intra-molecular energies as two main objectives to minimize.