Optimization of PID and FOPID controllers with new generation metaheuristic algorithms for controlling AVR system : concise survey
- Authors: Oladipo, S. , Sun, Y. , Wang, Z.
- Date: 2020
- Subjects: Automatic Voltage Regulator (AVR) , Proportional Integral Derivative (PID) controller , Metaheuristics
- Language: English
- Type: Conference proceedings
- Identifier: http://hdl.handle.net/10210/459766 , uj:40889 , Citation: Oladipo, S., Sun, Y. & Wang, Z. 2020. Optimization of PID and FOPID controllers with new generation metaheuristic algorithms for controlling AVR system : concise survey.
- Description: Abstract: Automatic Voltage Regulator (AVR) system is one of the major devices broadly used in many industrial applications for regulating the voltage of the synchronous generator within its nominal values. Consequently, providing a suitable controller for the AVR system becomes a necessity to prevent instability and error in the system’s output response. Studies from past works have shown that an adequately tuned PID controller will maximize the efficiency of the AVR system. In recent decades metaheuristic algorithms have become increasingly prevalent due to their tremendous success in addressing real-world optimization problems. This paper, therefore, presents a concise survey of the optimization of the PID and FOPID controllers with new generation metaheuristic algorithms for controlling the AVR system. A short description of each algorithm is presented with papers published in various reputable journals. Finally, the paper presents some future directions of research.
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Grouping genetic algorithm for industrial engineering applications
- 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.
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Simulated metamorphosis - a novel optimizer
- 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.
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