Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

Sönmez, Yusuf; Kahraman, Hamdi Tolga; Döşoğlu, M. Kenan; Güvenç, Uğur; Duman, Serhat

doi:10.1080/0952813x.2016.1198935

Cited by 39 publications

(16 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finding the best compromising solution in view of all the objectives also becomes difficult since the optimization algorithms are originally designed to optimize a single objective. 34 In recent years, SOS algorithm and some of its variants have been widely applied in solving various power engineering optimization problems, such as economic load dispatch, [35][36][37][38] optimal power flow, 39 congestion management, 40 and optimal DG allocation problem. In the weighted sum approach, a single solution is obtained that depends on the selected weights for each objective.…”

Section: Introductionmentioning

confidence: 99%

“…The SOS is a swarm intelligence-based algorithm that mimics the symbiotic interaction between different living organisms in the ecosystem. 34 In recent years, SOS algorithm and some of its variants have been widely applied in solving various power engineering optimization problems, such as economic load dispatch, [35][36][37][38] optimal power flow, 39 congestion management, 40 and optimal DG allocation problem. [41][42][43] Recently, an improved version of SOS, namely, chaotic SOS (CSOS), is proposed by Saha and Mukherjee.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Saha

Mukherjee

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This article presents a new method to decide the optimal locations and sizes of distributed generators (DGs) in radial distribution system (RDS). This task is associated with minimization of total voltage deviation and power loss and maximization of voltage stability index of the RDS. To solve this multiobjective DG allocation problem, a novel metaheuristic algorithm, namely, multiobjective chaotic symbiotic organisms search (SOS) (MOCSOS), is proposed in this article. The proposed MOCSOS is an improved version of the traditional SOS, where a chaotic local search technique is incorporated within the traditional SOS framework. The MOCSOS utilizes a fast nondominated sorting strategy to achieve the Pareto solutions. The constraints associated with this DG allocation problem are handled using a binary tournament selection–based approach. The performance of MOCSOS is validated on 33‐ and 69‐node RDSs. The efficacy of the MOCSOS is established by comparing its performance with other nature‐inspired state‐of‐the‐art algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Saha

Mukherjee

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Since its introduction in the literature, SOS has attracted the attention of numerous researchers from different domains. This led to its application in different fields of studies, namely engineering [ 28 , 29 ], power systems [ 30 – 32 ], cloud computing [ 33 ], and distribution scheduling [ 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times

2018

View full text Add to dashboard Cite

This paper addresses the problem of makespan minimization on unrelated parallel machines with sequence dependent setup times. The symbiotic organisms search (SOS) algorithm is a new and popular global optimization technique that has received wide acceptance in recent years from researchers in continuous and discrete optimization domains. An improved SOS algorithm is developed to solve the parallel machine scheduling problem. Since the standard SOS algorithm was originally developed to solve continuous optimization problems, a new solution representation and decoding procedure is designed to make the SOS algorithm suitable for the unrelated parallel machine scheduling problem (UPMSP). Similarly, to enhance the solution quality of the SOS algorithm, an iterated local search strategy based on combining variable numbers of insertion and swap moves is incorporated into the SOS algorithm. More so, to further improve the SOS optimization speed and performance, the longest processing time first (LPT) rule is used to design a machine assignment heuristic that assigns processing machines to jobs based on the machine dynamic load-balancing mechanism. Subsequently, the machine assignment scheme is incorporated into SOS algorithms and used to solve the UPMSP. The performances of the proposed methods are evaluated by comparing their solutions with other existing techniques from the literature. A number of statistical tests were also conducted to determine the variations in performance for each of the techniques. The experimental results showed that the SOS with LPT (SOS-LPT) heuristic has the best performance compared to other tested method, which is closely followed by SOS algorithm, indicating that the two proposed algorithms’ solution approaches are reasonable and effective for solving large-scale UPMSPs.

show abstract

“…Additionally, the stochastic programming method [2,3] and the robust optimization model [4,5] have been studied. On the other hand, different evolutionary algorithms have been implemented for the dynamic economic dispatch problem [6,7]. The pollutant emission caused by thermal plants was not considered in the above studies.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment

Zhang¹,

Liu²,

Liao³

et al. 2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

This paper establishes a probabilistic scenario-based framework for the stochastic dynamic economic emission dispatch with unit commitment (SDEED-UC) problem, by considering wind power integration. The scenario generation and reduction method are implemented to describe wind power uncertainty. Accordingly, each wind power scenario is analyzed separately to determine the on/off status of the units. As for a predetermined significance level, the UC scheduling solution can be obtained with a probabilistic point of view, considering all the original scenarios. Then the SDEED problem is converted into a number of deterministic scheduling problems. For each scenario in the reduced set, an enhanced multiobjective particle swarm optimization algorithm is proposed to produce the Pareto optimal solutions.The practicability and performance of the proposed approach are illustrated through a case study, and the results are compared with the existing multiobjective evolutionary algorithms.

show abstract

Symbiotic organisms search algorithm for dynamic economic dispatch with valve-point effects

Cited by 39 publications

References 27 publications

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times

A probabilistic scenario-based framework for solving stochastic dynamic economic emission dispatch with unit commitment

Contact Info

Product

Resources

About