Book Chapter10.1007/978-0-387-76813-7_20
Fuzzy Optimization via Multi-Objective Evolutionary Computation for Chocolate Manufacturing
Fernando Jiménez,Gracia Sánchez,Pandian Vasant,José L. Verdegay +3 more
- 01 Jan 2008
- pp 523-537
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TL;DR: The authors have analyzed using a modified S-curve membership function for the fuzziness patterns and fuzzy sensitivity of the solution found from the multi-objective optimization methodology.
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Abstract: This chapter outlines, first, a real-world industrial problem for product mix selection involving 8 variables and 21 constraints with fuzzy coefficients and, second, a multi-objective optimization approach to solve the problem. This problem occurs in production planning in which a decision maker plays a pivotal role in making decisions under a fuzzy environment. Decision maker should be aware of his/her level-of-satisfaction as well as degree of fuzziness while making the product mix decision. Thus, the authors have analyzed using a modified S-curve membership function for the fuzziness patterns and fuzzy sensitivity of the solution found from the multi-objective optimization methodology. An ad hoc Pareto-based multi-objective evolutionary algorithm is proposed to capture multiple nondominated solutions in a single run of the algorithm. Results obtained have been compared with the well-known multi-objective evolutionary algorithm NSGA-II.
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Citations
A novel method for fuzzy diagnostic system design.
Mostafa Langarizadeh,Azam Orooji +1 more
TL;DR: A classification method was developed to predict liver disease which in addition to high classification accuracy, it has been created without expert knowledge and provided an understandable explanation of data.
97
Solving Fuzzy Optimization Problems of Uncertain Technological Coefficients with Genetic Algorithms and Hybrid Genetic Algorithms Pattern Search Approaches
Pandian Vasant
- 01 Jan 2012
TL;DR: In this chapter a solution is proposed to a certain nonlinear programming difficulties related to the presence of uncertain technological coefficients represented by vague numbers, and only vague numbers with modified s-curve membership functions are considered.
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Hybrid LS–SA–PS methods for solving fuzzy non-linear programming problems
TL;DR: An industrial production planning problem with a cubic objective function, eight decision variables and 29 constraints was solved successfully using the LS–SA–PS hybrid optimization techniques and strongly suggests that the hybrid LS-SA-PS algorithm is very efficient and productive in solving the large scale non-linear fuzzy programming problem.
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
•Book
Decision-making in a fuzzy environment
Richard Bellman,Lotfi A. Zadeh +1 more
- 01 Jan 1970
TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.
•Book
Evolutionary algorithms for solving multi-objective problems
Gary B. Lamont,David A. Van Veldhuizen +1 more
- 30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
Kalyanmoy Deb,Samir Agrawal,Amrit Pratap,T. Meyarivan +3 more
- 18 Sep 2000
TL;DR: Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.