Simulated metamorphosis - a novel optimizer
- Mutingi, Michael, Mbohwa, Charles
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Metamorphosis , Evolution , Optimization , Algorithm , Metaheuristics , Simulated metamorphosis
- Type: Article
- Identifier: uj:4972 , ISSN 2078-0966 , http://hdl.handle.net/10210/13073
- Description: This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times.
- Full Text:
- Authors: Mutingi, Michael , Mbohwa, Charles
- Date: 2014
- Subjects: Metamorphosis , Evolution , Optimization , Algorithm , Metaheuristics , Simulated metamorphosis
- Type: Article
- Identifier: uj:4972 , ISSN 2078-0966 , http://hdl.handle.net/10210/13073
- Description: This paper presents a novel metaheuristic algorithm, simulated metamorphosis (SM), inspired by the biological concepts of metamorphosis evolution. The algorithm is motivated by the need for interactive, multi-objective, and fast optimization approaches to solving problems with fuzzy conflicting goals and constraints. The algorithm mimics the metamorphosis process, going through three phases: initialization, growth, and maturation. Initialization involves random but guided generation of a candidate solution. After initialization, the algorithm successively goes through two loops, that is, growth and maturation. Computational tests performed on benchmark problems in the literature show that, when compared to competing metaheuristic algorithms, SM is more efficient and effective, producing better solutions within reasonable computation times.
- Full Text:
Grouping genetic algorithm for industrial engineering applications
- Mutingi, Michael, Mapfaira, Herbert, Dube, Partson
- Authors: Mutingi, Michael , Mapfaira, Herbert , Dube, Partson
- Date: 2013
- Subjects: Grouping problems , Grouping genetic algorithms , Genetic algorithms , Metaheuristics , Industrial engineering
- Type: Article
- Identifier: uj:4734 , http://hdl.handle.net/10210/11560
- Description: Industry is inundated with grouping problems concerned with formation of groups or clusters of system entities for the purpose of improving the overall system efficiency and effectiveness. Various extant grouping problems include cell formation problem, vehicle routing problem, bin packing problem, truck loading, home healthcare scheduling, and task assignment problem. Given the widespread grouping problems in industry, it is important to develop a tool for solving such problems from a common view point. This paper seeks to identify common grouping problems, identify their common grouping structures, present an outline of group genetic algorithm (GGA), and map the problems to the GGA approach. The practicality of the GGA tool in is highly promising in Industrial Engineering applications.
- Full Text:
- Authors: Mutingi, Michael , Mapfaira, Herbert , Dube, Partson
- Date: 2013
- Subjects: Grouping problems , Grouping genetic algorithms , Genetic algorithms , Metaheuristics , Industrial engineering
- Type: Article
- Identifier: uj:4734 , http://hdl.handle.net/10210/11560
- Description: Industry is inundated with grouping problems concerned with formation of groups or clusters of system entities for the purpose of improving the overall system efficiency and effectiveness. Various extant grouping problems include cell formation problem, vehicle routing problem, bin packing problem, truck loading, home healthcare scheduling, and task assignment problem. Given the widespread grouping problems in industry, it is important to develop a tool for solving such problems from a common view point. This paper seeks to identify common grouping problems, identify their common grouping structures, present an outline of group genetic algorithm (GGA), and map the problems to the GGA approach. The practicality of the GGA tool in is highly promising in Industrial Engineering applications.
- Full Text:
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