Discussing the article: "Population optimization algorithms: Mind Evolutionary Computation (MEC) algorithm"
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Check out the new article: Population optimization algorithms: Mind Evolutionary Computation (MEC) algorithm.
The article considers the algorithm of the MEC family called the simple mind evolutionary computation algorithm (Simple MEC, SMEC). The algorithm is distinguished by the beauty of its idea and ease of implementation.
Population algorithms used in evolutionary calculations have a number of advantages over classical algorithms when solving complex high-dimensional problems. They can more efficiently find suboptimal solutions that are close enough to the optimal one, which is often acceptable in practical optimization problems.
One interesting approach in evolutionary computing is the Mind Evolutionary Computation (MEC) algorithm proposed in 1998 by Chengai and his co-authors. Unlike the expected modeling of the human brain, the MEC algorithm models some aspects of human behavior in society. In this algorithm, each individual is considered as an intelligent agent functioning in a group of people. When making decisions, an individual feels influenced both by members of his group and by members of other groups. To achieve a high position in society, an individual has to learn from the most successful individuals in his group. At the same time, in order for his group to become more successful than other groups, all individuals must be guided by the same principle in intergroup competition. An important aspect of the MEC algorithm is the exchange of information between individuals within a group and between groups. This reflects the need for continuous and free exchange of information for the successful development of a society of intelligent individuals.
MEC algorithms implement the presented concept using local competition and dissimilation operations responsible for local and global search, respectively. Message boards are used by the algorithm to store information about the evolutionary history of the population. The optimization process is controlled based on this information.Author: Andrey Dik