The new representation proposed in this paper deals with the partitional clustering problem by regarding it as a multi-objective optimal problem;in this approach the niche Pareto genetic algorithm is used to solve the problem.

This paper introduce a new and efficient multi-objective evolutionary algorithms (EMOEA) for solving unconstrained and constrained multi-objective optimal problems (MOPs).

A Framework for Multi-objective Optimization Problem Solving

In the mid-1980s, Evolutionary Algorithm was introduced to solve the Multi-objective Optimization Problem.

The multi-objective optimization problem based on pareto is a important research direction of the evolutionary algorithm,and how to improve the efficiency of constructing the Pareto non-dominated set is a key to the algorithm.

Novelε-dominance multi-objective evolutionary algorithms for solving DRS multi-objective optimization problems;

Genetic algorithms based on colony searching are especially suitable for solving multi-objective optimization problems.

The algorithms which employ this crossover operator behave very well when they are used to solve Multi-objective Optimization Problem and , to a certain extent, solve the puzzle, i.

Used the idea and the selection mechanism called replicator dynamics of evolutionary game theory for reference, and proposed an evolutionary game based optimization algorithm (EGOA) to solve multiobjective optimization problems (MOPs).