Classifying fuzzy multi-criterion decision making and evolutionary algorithm

초록

During last two decades, hybrid computational intelligence is inspired by nature intelligence and multi-criteria decision making. Nature intelligence is global optimization framework as a collection of algorithms for dominating robotic swarm due to superb potency of the natural swarm systems. These techniques work in a manner that mimics the behavior of swarms. Swarms are basically large numbers of homogenized and easy agents, that interacts among themselves and their environment locally with none central management. The other important aspect for hybrid computational intelligence is multi-criterion decision problem. Decision making is the process of selecting the best alternative where precision of data plays a major role. In the problem of decision making, sometimes, several criteria are considering at a time. Experts are needed to find quantitative and qualitative decision for finding the performance of every possible alternative with regards to every criterion. This chapter provides an introduction to hybrid computational intelligence. It covers a brief review of optimization, and meta-heuristic. It also examines the scope of swarm intelligence in overcoming the limitations of traditional methods. It thoroughly covers Ant Colony Optimization and Swarm Optimization. The chapter then briefly introduces Multi criteria decision problem and various tools for these problems such as WSM, WPM, AHP, TOPSIS, ELECTRE, and VIKOR. This chapter discusses on the behavior and application of these tools. © 2021 Scrivener Publishing LLC.

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