Soccer league optimization for load flow analysis of power systems

Srilakshmi, Koganti; Babu, P. Ravi; Venkatesan, Yogambari; Palanivelu, Aravindhababu

doi:10.1002/jnm.2965

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…32 A population oriented algorithm known as soccer league optimization (SLO) that imitates the behavior of team players in a soccer league competition was proposed by researchers to solve the load flow (LF) problem for addressing the active-reactive power and voltage magnitude and angle related concerns of IEEE transmission and distribution systems. 33 A convergence particle-based weight factor based improved wind-driven algorithm (IWDA) was proposed to optimize AESs integrated Indian utility systems. 34 Green Leafhopper Flame optimization algorithm (GLFOA), a novel intelligent optimization algorithm introducing the photo-tactic swarming behavior of green leafhoppers, was developed and applied for tuning various system controllers to address the performance issues of hybrid microgrid-based energy systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers implemented a modified sine cosine algorithm (MSCA) to obtain optimal ratings of the conventional PID and fuzzy‐PID (FPID) controllers in the AGC problem 32 . A population oriented algorithm known as soccer league optimization (SLO) that imitates the behavior of team players in a soccer league competition was proposed by researchers to solve the load flow (LF) problem for addressing the active‐reactive power and voltage magnitude and angle related concerns of IEEE transmission and distribution systems 33 . A convergence particle‐based weight factor based improved wind‐driven algorithm (IWDA) was proposed to optimize AESs integrated Indian utility systems 34 .…”

Section: Introductionmentioning

confidence: 99%

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Chaotic simplex moth swarm algorithm for optimization of alternative energy integrated power grid including static synchronous series compensator

Banerjee¹,

Roy

Panda³

2022

Int J Numerical Modelling

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An optimal power flow (OPF) model is developed in this paper, which includes three highly volatile energy sources: wind energy, photovoltaic energy, and electric vehicle as a vehicle to grid energy source. An interpretable probabilistic approach is implemented to estimate the power generation from the mentioned energy sources using different probability distribution functions. A single static synchronous series compensator is optimally configured and positioned in the combined grid power system to achieve balanced operation. This significant uncertainty imposed OPF model is solved by blending (a) chaotic mapping technique, and (b) Nelder–mead simplex method with conventional moth swarm algorithm (MSA) and the modified version of MSA is called chaotic simplex MSA (CSMSA). Proposed CSMSA's performance is evaluated analytically using extensive simulation approaches that include six different power‐related simulation instances considering IEEE 30 and 118‐bus test grid systems. The strength of the proposed CSMSA is examined by comparing its performance to five outstanding metaheuristic techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chaotic simplex moth swarm algorithm for optimization of alternative energy integrated power grid including static synchronous series compensator

Banerjee¹,

Roy

Panda³

2022

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…The volleyball premier league (VPL) algorithm was developed based on interaction of volleyball teams during one season [ 57 ]. Some other game-based algorithms are an improved football game optimizer (IFGO) [ 58 ], the running city game optimizer (RCGO) [ 59 ], Nash game-efficient global optimizer (Nash-EGO) [ 60 ], and a generalized soccer league optimizer (SLO) [ 61 ].…”

Section: Introductionmentioning

confidence: 99%

Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

2023

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Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications.

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Socio-inspired evolutionary algorithms: a unified framework and survey

2023

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Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin's ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

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Soccer league optimization for load flow analysis of power systems

Cited by 5 publications

References 27 publications

Chaotic simplex moth swarm algorithm for optimization of alternative energy integrated power grid including static synchronous series compensator

Chaotic simplex moth swarm algorithm for optimization of alternative energy integrated power grid including static synchronous series compensator

Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

Socio-inspired evolutionary algorithms: a unified framework and survey

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