An Adaptive Multiobjective Evolutionary Algorithm for Economic Emission Dispatch
- Resource Type
- Conference
- Authors
- Chiang, Tsung-Che; Visutarrom, Thammarsat; Kulturel-Konak, Sadan; Konak, Abdullah
- Source
- 2022 IEEE Congress on Evolutionary Computation (CEC) Evolutionary Computation (CEC), 2022 IEEE Congress on. :1-8 Jul, 2022
- Subject
- Bioengineering
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Power demand
Costs
Heuristic algorithms
Evolutionary computation
Reinforcement learning
Power generation
economic dispatch
emission
multiobjective
evolutionary algorithm
adaptive control
reinforcement learning
parameter control
- Language
This paper addresses the economic emission dispatch (EED) problem where the goal is to allocate the power output of power generation units to satisfy power demand and minimize the cost and emissions simultaneously. We propose a multiobjective differential evolution algorithm and a reinforcement learning technique to adaptively control the parameters of differential evolution. Moreover, the proposed approach utilizes mating restriction and preferences in mating selection to improve search effectiveness and a dynamically controlled mutation to increase the exploration ability. The proposed ideas and algorithm were examined using four EED test cases. Experimental results showed positive effects of our proposed methods and the competitive performance of our algorithm.