| There are many multi-objective optimization problems in the real world. The multi-objective optimization is a rising subject in the recent 30 years. Many researchers have been finding some important techniques to deal with multi-objective optimization problems.Evolutionary Algorithms (EA) have some advantages in the complex multi-objective optimization problems. Schaffer firstly used VEGA to optimize multi-objective problems in the mid -1980s. Most of the approaches can be put into three categories: (1) Aggregating approaches: this approach combines objectives into a single function to optimize. Although it is very simple and easy to implement, this approach may be difficult to generate a set of weights that properly scales the objectives when little is known about the problem. (2) Population-based non-Pareto approaches, e.g. Vector Evaluated Genetic Algorithms (VEGA): this approach performs the proportional selection according to each objective function. Although it modifies the selection mechanism of the GA, this approach behaves as an aggregating approach. (3) Pareto-based approaches, e.g. Strength Pareto Evolutionary Algorithm (SPEA): this approach is outstanding in the approaches to evolutionarymulti-objective optimization.To overcome the problems of GA, Mind Evolutionary Computation (MEC) was proposed by Chengyi Sun in 1998, which imitates two phenomena of human society - similartaxis and dissimilation. After several years' studies on its theory and experiment, MEC has been made great progress. So far, a preliminary system has already been established for MEC.This paper proposes two kinds of multi-objective optimization algorithms, respectively called Pareto Mind Evolutionary Computation (Pareto-MEC) and Scored Pareto Mind Evolutionary Computation (SP-MEC). They use MEC to solve the multi-objective optimization problems.The principles of Pareto-MEC are: (1) A number of individuals are scattered in the whole solution space, and then some better individuals of them are selected as the initial centers for every group. (2) Each group only searches a local area and gradually shifts from its initial center to the Pareto front. (3) During the process of shift to this front, this algorithm would bound the searching region of the group and control the shifting direction of the group. Both of above function (1) and function (3) are called as dissimilation, and function (2) is called as similartaxis.The principles of SP-MEC are: (1) A number of individuals are scattered in the whole solution space, and then some better individuals of them are selected as the initial centers for every group according to their scores. (2) Each group only searches a local area and gradually shifts from its initial center to the Pareto front. (3) During the process of shift to this front, this algorithm would bound the searching region of the group and control theshifting direction of the group. Both of above function (1) and function (3) are called as dissimilation, and function (2) is called as similartaxis.Pareto-MEC and SP-MEC are respectively compared with the reference algorithms of Rand, VEGA, NSGA and SPEA. The test functions used in the experiment are a suit of four different test problems: convexity, non-convexity, discreteness and non-uniformity. On all test problems, both Pareto-MEC and SP-MEC outperform Rand, VEGA and NSGA; Pareto-MEC is as good as SPEA on the first three test problems, but SP-MEC is better than SPEA; They beat SPEA on the last test problem. Different from the reference algorithms that use the pre-specified generation number as their terminations, Pareto-MEC and SP-MEC have an objective termination criterion that can ensure the quality of solutions and the computational efficiency.Two evaluative methods: Cover and Spacing are used as the quantificational criterion for our algorithms on the test functions: convexity, non-convexity and non-uniformity. The experiment shows from the angle of arith that the performances of Pareto-MEC and SP-MEC are better than SPEA which is one of the excellenc...
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