Multiobjective evolutionary algorithms for strategic deployment of resources in operational units
نویسندگان
چکیده
منابع مشابه
Evolutionary Algorithms for Multiobjective Optimization
Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separat...
متن کاملInteractive Multiobjective Evolutionary Algorithms
This chapter describes various approaches to the use of evolutionary algorithms and other metaheuristics in interactive multiobjective optimization. We distinguish the traditional approach to interactive analysis with the use of single objective metaheuristics, the semi-a posteriori approach with interactive selection from a set of solutions generated by a multiobjective metaheuristic, and spec...
متن کاملMultiobjective optimization using evolutionary algorithms
Evolutionary algorithms (EAs) such as evolution strategies and genetic algorithms have become the method of choice for optimization problems that are too complex to be solved using deterministic techniques such as linear programming or gradient (Jacobian) methods. The large number of applications (Beasley (1997)) and the continuously growing interest in this field are due to several advantages ...
متن کاملApplication Issues for Multiobjective Evolutionary Algorithms
Abstract: Various issues of the design and application of multiobjective evolutionary algorithms to real-life optimization problems are discussed. In particular, questions on problem-specific data structures and evolutionary operators and the determination of method parameters are treated. Three application examples in the areas of constrained global optimization (electronic circuit design), se...
متن کاملParallel Evolutionary Algorithms for Multiobjective Placement Problem
Non-deterministic iterative heuristics such as Tabu Search (TS), Simulated Evolution (SimE), Simulated Annealing (SA), and Genetic Algorithms (GA) are being widely adopted to solve a range of hard optimization problems [1]. This interest is attributed to their generality, ease of implementation, and their ability to deliver high quality results. However, depending on the size of the problem, su...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: European Journal of Operational Research
سال: 2020
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2019.02.002