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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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An interactive fuzzy satisficing method for multiobjective nonconvex programming problems with fuzzy numbers through
Summary
This study introduces a method for solving fuzzy multiobjective nonconvex nonlinear programming problems. It uses an interactive fuzzy satisficing approach with evolutionary algorithms to find optimal solutions efficiently.
Area of Science:
- Operations Research
- Computational Intelligence
- Decision Sciences
Background:
- Multiobjective nonconvex nonlinear programming problems often involve parameters with fuzzy characteristics.
- Existing methods may struggle with the inherent uncertainty and complexity of these fuzzy parameters.
Purpose of the Study:
- To formulate multiobjective nonconvex nonlinear programming problems incorporating fuzzy numbers.
- To develop an interactive fuzzy satisficing method using coevolutionary genetic algorithms.
- To enhance the efficiency and applicability of existing algorithms for fuzzy optimization.
Main Methods:
- Formulation of fuzzy multiobjective nonconvex nonlinear programming problems.
- Introduction of nonfuzzy alpha-programming problems using alpha-level sets.
- Application of a coevolutionary genetic algorithm (GENOCOP III) to solve augmented minimax problems.
- Development of a revised GENOCOP III with improved methods for generating feasible points.
Main Results:
- An extended Pareto optimal solution set is obtainable by specifying fuzzy goals, alpha-degree, and reference membership values.
- The revised GENOCOP III demonstrates improved efficiency in generating feasible points.
- An interactive fuzzy satisficing method effectively derives satisficing solutions from the extended Pareto optimal set.
Conclusions:
- The proposed interactive fuzzy satisficing method provides an efficient approach for decision-makers to find solutions for fuzzy multiobjective nonconvex nonlinear programming problems.
- The revised GENOCOP III algorithm effectively addresses limitations of previous methods, enhancing computational efficiency.
- The study offers a practical framework for handling uncertainty in complex optimization scenarios.
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