Multi-objective optimization - WikipediaIn almost no other field of computer science, the idea of using bio-inspired search paradigms has been so useful as in solving multiobjective optimization problems. The idea of using a population of search agents that collectively approximate the Pareto front resonates well with processes in natural evolution, immune systems, and swarm intelligence. This tutorial will review some of the most important fundamentals in multiobjective optimization and then introduce representative algorithms, illustrate their working principles, and discuss their application scope. In addition, the tutorial will discuss statistical performance assessment. Finally, it highlights recent important trends and closely related research fields. The tutorial is intended for readers, who want to acquire basic knowledge on the mathematical foundations of multiobjective optimization and state-of-the-art methods in evolutionary multiobjective optimization.
A tutorial on multiobjective optimization: fundamentals and evolutionary methods
Alert Subscription. DOI: Recommend this eBook to your Library. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective more than three objectives optimization and parallel computation.
The mathematical foundations of programming relate to two significant contributions, namely the Theory of Games and Economic Behavior by John von Neumann and Oskar Morgenstern in , and the simplex method discovered by George B. Dantzig in Paradoxically, the origin of multi-objective optimization MOO goes back to earlier works by Francis Y. Edgeworth in , and Vilfredo Pareto in The two leading economists developed the theory of indifference curves and defined the basic concept of optimality for problems where multiple objectives are to be optimized. In such problems, the objectives are often conflicting. The goal is to find trade-off solutions that represent best compromises among the objectives.
PDF | On Jan 1, , Antonio López Jaimes and others published An Introduction to Multiobjective Optimization Techniques. In: Book Title. Editor: E ditor Name, . Evolutionary Multiobjective Optimization (EMO) is another approach useful.
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Multi-objective optimization also known as multi-objective programming , vector optimization , multicriteria optimization , multiattribute optimization or Pareto optimization is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.
Search Methodologies pp Cite as. Many real-world search and optimization problems are naturally posed as non-linear programming problems having multiple objectives. Due to the lack of suitable solution techniques, such problems were artificially converted into a single-objective problem and solved. The difficulty arose because such problems give rise to a set of trade-off optimal solutions known as Pareto-optimal solutions , instead of a single optimum solution. It then becomes important to find not just one Pareto-optimal solution, but as many of them as possible.