Solving of economical load dispatch using efficient group leader optimization technique

Shah, Kathan; Bhattacharjee, Kuntal; Godwal, Shanker D.

doi:10.1201/9780429321573-33

Cited by 3 publications

(1 citation statement)

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“…In practical ELD problems, other constraints should consider, like valve point effect, ramp rate, and prohibited operating zones(POZ). This ELD problem is initially solved by some classical methods like quadratic programming (Shah et al, 2019), Dynamic Programming, Linear Programming, gradient method, Lagrangian relaxation, and Hopfield framework (Dieu & Schegner, 2012). The main issue with this method is that they are susceptible to starting points and mostly converge and diverge at a local optimum solution.…”

Section: Introductionmentioning

confidence: 99%

Artificial Electric Field Algorithm Applied to the Economic Load Dispatch Problem With Valve Point Loading Effect

Shah

Soni

Bhattacharjee

2023

International Journal of Swarm Intelligence Research

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Economic load dispatch is to operate thermal generators economically with fulfilling load demand. This economic dispatch problem becomes highly complex and non-linear after considering various operating constraints like valve-point loading effect, generator operating constraints, and prohibited operating zone. The recently developed physics law-based artificial electric field algorithm has been applied to solve highly complex and non-linear ELD problems. The exploration and exploitation strategy of the algorithm helps to avoid local optimum value, and to get global optimum value in less computation time. The AEFA method has been applied to 10, 13, 15, 40, and large 110 thermal generators to validate the effectiveness of the proposed algorithm. The results obtained by the proposed algorithm have been compared with other recently developed algorithms.

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Section: Introductionmentioning

confidence: 99%

Artificial Electric Field Algorithm Applied to the Economic Load Dispatch Problem With Valve Point Loading Effect

Shah

Soni

Bhattacharjee

2023

International Journal of Swarm Intelligence Research

Self Cite

View full text Add to dashboard Cite

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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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