Solving combined heat and power economic dispatch problem using real coded genetic algorithm with improved Mühlenbein mutation

•An improved Mühlenbein mutation is proposed for GA algorithm.•Proposed algorithm is evaluated using different benchmark functions.•The proposed algorithm has shown better convergence and constraint handling capability.•Proposed algorithm found lower cost for CHPED problem in comparison with other a...

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Published inApplied thermal engineering Vol. 99; pp. 465 - 475
Main Authors Haghrah, A., Nazari-Heris, M., Mohammadi-ivatloo, B.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 25.04.2016
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Online AccessGet full text
ISSN1359-4311
DOI10.1016/j.applthermaleng.2015.12.136

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Abstract •An improved Mühlenbein mutation is proposed for GA algorithm.•Proposed algorithm is evaluated using different benchmark functions.•The proposed algorithm has shown better convergence and constraint handling capability.•Proposed algorithm found lower cost for CHPED problem in comparison with other algorithms. The combined heat and power economic dispatch (CHPED) is a complicated optimization problem which determines the production of heat and power units to obtain the minimum production costs of the system, satisfying the heat and power demands and considering operational constraints. This paper presents a real coded genetic algorithm with improved Mühlenbein mutation (RCGA-IMM) for solving CHPED optimization task. Mühlenbein mutation is implemented on basic RCGA for speeding up the convergence and improving the optimization problem results. To evaluate the performance features, the proposed RCGA-IMM procedure is employed on six benchmark functions. The effect of valve-point and transmission losses is considered in cost function and four test systems are presented to demonstrate the effectiveness and superiority of the proposed method. In all test cases the obtained solutions utilizing RCGA-IMM optimization method are feasible and in most instances express a marked improvement over the provided results by recent works in this area.
AbstractList •An improved Mühlenbein mutation is proposed for GA algorithm.•Proposed algorithm is evaluated using different benchmark functions.•The proposed algorithm has shown better convergence and constraint handling capability.•Proposed algorithm found lower cost for CHPED problem in comparison with other algorithms. The combined heat and power economic dispatch (CHPED) is a complicated optimization problem which determines the production of heat and power units to obtain the minimum production costs of the system, satisfying the heat and power demands and considering operational constraints. This paper presents a real coded genetic algorithm with improved Mühlenbein mutation (RCGA-IMM) for solving CHPED optimization task. Mühlenbein mutation is implemented on basic RCGA for speeding up the convergence and improving the optimization problem results. To evaluate the performance features, the proposed RCGA-IMM procedure is employed on six benchmark functions. The effect of valve-point and transmission losses is considered in cost function and four test systems are presented to demonstrate the effectiveness and superiority of the proposed method. In all test cases the obtained solutions utilizing RCGA-IMM optimization method are feasible and in most instances express a marked improvement over the provided results by recent works in this area.
Author Nazari-Heris, M.
Mohammadi-ivatloo, B.
Haghrah, A.
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  surname: Mohammadi-ivatloo
  fullname: Mohammadi-ivatloo, B.
  email: bmohammadi@tabrizu.ac.ir
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Keywords Real code genetic algorithm (RCGA)
Combined heat and power (CHP)
Economic dispatch
Non-convex optimization problem
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Snippet •An improved Mühlenbein mutation is proposed for GA algorithm.•Proposed algorithm is evaluated using different benchmark functions.•The proposed algorithm has...
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SubjectTerms Combined heat and power (CHP)
Economic dispatch
Non-convex optimization problem
Real code genetic algorithm (RCGA)
Title Solving combined heat and power economic dispatch problem using real coded genetic algorithm with improved Mühlenbein mutation
URI https://dx.doi.org/10.1016/j.applthermaleng.2015.12.136
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