Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling

Social Group Optimization (SGO), developed by Satapathy et al. in the year 2016, is a class of meta-heuristic optimization inspired by social behavior. It has two phases: improving phase and acquiring phase. In the improving phase, each individual improves its knowledge by interacting with the best...

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Vydáno v:Applied soft computing Ročník 95; s. 106524
Hlavní autoři: Naik, Anima, Satapathy, Suresh Chandra, Abraham, Ajith
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.10.2020
Témata:
Benchmark functions
Fitness function evaluations
Hydrothermal scheduling problem
Meta-heuristic algorithm
Social Group Optimization
Benchmark functions
Hydrothermal scheduling problem
Social Group Optimization
Meta-heuristic algorithm
Fitness function evaluations
ISSN:1568-4946
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Abstract Social Group Optimization (SGO), developed by Satapathy et al. in the year 2016, is a class of meta-heuristic optimization inspired by social behavior. It has two phases: improving phase and acquiring phase. In the improving phase, each individual improves its knowledge by interacting with the best person/solution and in acquiring phase, the individuals interact with randomly selected individuals and the best person simultaneously to acquire knowledge. Modified Social Group Optimization (MSGO) is the improved version of SGO, where the acquiring phase is modified. A self-awareness probability factor is added in the acquiring phase, which enhances the learning capability of an individual from the best-learned person in the societal setup. It is observed that this modification has improved both exploration and exploitation abilities in comparison with the conventional SGO. To analyze the performance of the MSGO, an exhaustive performance comparison is made with GA, PSO, DE, ABC, and a few newer algorithms of the years 2010–2019. The results are tabulated in six experiments. Later, MSGO is applied to solve the short-term hydrothermal scheduling (HTS) problem. The central objective of the HTS problem is to ascertain the optimal plan of action for hydro and thermal generation minimizing the fuel cost of thermal plants and, at the same time satisfying various operational and physical constraints. The valve point loading effect related to the thermal power plants, transmission loss, and other constraints lead HTS as a complex non-linear, non-convex, and non-smooth optimization problem. Simulation results clearly show that the MSGO method is capable of obtaining a better solution. •This paper modifies the SGO algorithm to improve the performance of the algorithm.•Performance of MSGO is compared with 25 algorithms, proposed before and after it.•Performances of these algorithms are compared using many classical benchmark functions.•MSGO algorithm is applied to solve Short-term Hydrothermal Scheduling Problem.•MSGO algorithm has shown best competitive performance compare to all other algorithms.
AbstractList Social Group Optimization (SGO), developed by Satapathy et al. in the year 2016, is a class of meta-heuristic optimization inspired by social behavior. It has two phases: improving phase and acquiring phase. In the improving phase, each individual improves its knowledge by interacting with the best person/solution and in acquiring phase, the individuals interact with randomly selected individuals and the best person simultaneously to acquire knowledge. Modified Social Group Optimization (MSGO) is the improved version of SGO, where the acquiring phase is modified. A self-awareness probability factor is added in the acquiring phase, which enhances the learning capability of an individual from the best-learned person in the societal setup. It is observed that this modification has improved both exploration and exploitation abilities in comparison with the conventional SGO. To analyze the performance of the MSGO, an exhaustive performance comparison is made with GA, PSO, DE, ABC, and a few newer algorithms of the years 2010–2019. The results are tabulated in six experiments. Later, MSGO is applied to solve the short-term hydrothermal scheduling (HTS) problem. The central objective of the HTS problem is to ascertain the optimal plan of action for hydro and thermal generation minimizing the fuel cost of thermal plants and, at the same time satisfying various operational and physical constraints. The valve point loading effect related to the thermal power plants, transmission loss, and other constraints lead HTS as a complex non-linear, non-convex, and non-smooth optimization problem. Simulation results clearly show that the MSGO method is capable of obtaining a better solution. •This paper modifies the SGO algorithm to improve the performance of the algorithm.•Performance of MSGO is compared with 25 algorithms, proposed before and after it.•Performances of these algorithms are compared using many classical benchmark functions.•MSGO algorithm is applied to solve Short-term Hydrothermal Scheduling Problem.•MSGO algorithm has shown best competitive performance compare to all other algorithms.
ArticleNumber 106524
Author Naik, Anima
Satapathy, Suresh Chandra
Abraham, Ajith
Author_xml – sequence: 1
  givenname: Anima
  orcidid: 0000-0002-7808-5994
  surname: Naik
  fullname: Naik, Anima
  email: animanaik@klh.edu.in
  organization: Department of CSE, KL University, Hyderabad, Telangana, India
– sequence: 2
  givenname: Suresh Chandra
  surname: Satapathy
  fullname: Satapathy, Suresh Chandra
  email: suresh.satapathyfcs@kiit.ac.in
  organization: School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India
– sequence: 3
  givenname: Ajith
  surname: Abraham
  fullname: Abraham, Ajith
  email: ajith.abraham@ieee.org
  organization: Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA 98071, USA
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Keywords Benchmark functions
Hydrothermal scheduling problem
Social Group Optimization
Meta-heuristic algorithm
Fitness function evaluations
Language English
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Snippet Social Group Optimization (SGO), developed by Satapathy et al. in the year 2016, is a class of meta-heuristic optimization inspired by social behavior. It has...
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SubjectTerms Benchmark functions
Fitness function evaluations
Hydrothermal scheduling problem
Meta-heuristic algorithm
Social Group Optimization
Title Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling
URI https://dx.doi.org/10.1016/j.asoc.2020.106524
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