Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

Roy, Provas Kumar; Bhui, Sudipta

doi:10.1016/j.ijepes.2013.06.015

Cited by 178 publications

(113 citation statements)

References 41 publications

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“…Various optimizat ion techniques as lamda iteration [41], quadratic programming and GAMS [52], differential evolution(DE) [43], teaching learning based optimization(TLBO) [44], Chemical Reaction Optimization (CRO) [45], krill herd algorithm(KHA) [46,53] invasive weed optimization(IW O) [47], gravitational search (GSA ) [48], flower pollination algorith m(FPA) [49], Art ificial Bee Co lony(ABC) [50], rooted tree optimization(RTO) [51] and hybrid DE with particle swarm optimization (PSO) [52] are successfully applied for solution of ELD problem.A co mprehensive review of NI techniques for solution ELD problem is presented in [40] To validate the performance of proposed KHAMCD it is tested on two standards complex cons traint comparative mediu m and large scale ELD problem as below.…”

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

“…The statistical performance over twenty trials is tabulated in Table 9. The best operating cost achieved by the KHAM CD method is 136446.4053$/hr .The co mparison of result has been made with hybrid genetic algorithm with ant colony optimization (GAAPI) [42], shuffled differential evolution (SDE) [43], teaching learning based optimization (TLBO) [44], oppositional real coded chemical reaction optimizat ion (ORCCRO) [45] , krill herd algorith m (KHA) [46], oppositional invasive weed optimization (OIWO) [47] and most recently reported Opposition-based krill herd algorith m (OKHA) [53], hybrid PSO DE approach [54]. Here it can be observed that proposed method KHAMCD is able to achieve cheapest generation cost as compared to other reported methods.…”

Section: ) Forty Unit Nonconvex Systemmentioning

confidence: 99%

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Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Agrawal¹,

Pandit²,

Dubey³

2016

IJISA

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Abstract-Krill herd algorith m (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, mo ment induced by other krill, fo raging motion and random physical d iffusion. These three key features of the algorith m provide a good balance between global and local search capability, which makes the algorith m very powerfu l. The objective is to minimize the distance of each krill fro m the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH A lgorith m. KHA with mutation and crossover is called as (KHAM C) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two imp roved variants KHAMC and KHAM CD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms. Index Terms-Krill Herd A lgorith m (KHA), mutation and crossover, neighborhood distance concept, unimodal function, multimodal function, economic load dispatch.

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Section: Economic Load Dispatch Problemmentioning

confidence: 99%

Section: ) Forty Unit Nonconvex Systemmentioning

confidence: 99%

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Agrawal¹,

Pandit²,

Dubey³

2016

IJISA

View full text Add to dashboard Cite

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“…It consists of six thermal generating units [7]. The fuel cost and emission function is convex in nature.…”

Section: A Desescription Of Test Systems 1) Test System-1mentioning

confidence: 99%

“…", and for test system 4,5 and 6 in " Table 2.". For test system 1, the outcome of simulation result obtained by SFSA and the proposed ISFSA in terms of best cost is found to be 63975.9724 $/hr and 63975.7780 $/hr respectively, the corresponding dispatch solution is presented in " The results have been compared with Differential Evolution (DE) [3], Quasi -oppositional Teacher Learner based Optimization (QTLBO) [7] and most recently reported method Backtracking Search Algorithm (BSA) [10] and presented in " Table 3.". Here it is observed that the best cost solution obtained by SFSA is also found to be better than other reported method.…”

Section: B Best Cost Solutionmentioning

confidence: 99%

“…But in the last decade various nature inspired algorithm were developed to solve the EED problem like Differential Evolution (DE) [3], simulated annealing (SA) algorithm [4], Bacterial foraging algorithm (BFA) [5]- [6] Teaching learning based optimization (TLBO) [7], gravitational search algorithm(GSA) [8], Real coded Chemical Reaction algorithm(RCCRO) [9], Backtracking search algorithm (BSA) [10] and etc. Also algorithm likeCuckoo Search Algorithm (CSA) [25],Cat Swarm Optimization (CSO) [26] and Ant Colony Optimization(ACO) [27] are used for optimization of real world problem.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Optimal Dispatch Solution of Solar-Wind-Thermal System Using Improved Stochastic Fractal Search Algorithm

Tyagi¹,

Dubey²,

Pandit³

2016

IJITCS

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Abstract-This paper presents solution of multi-objective optimal dispatch (MOOD) problem of solar-wind-thermal system by improved stochastic fractal search (ISFSA) algorithm. Stochastic fractal search (SFSA) is inspired by the phenomenon of natural growth called fractal. It utilizes the concept of creating fractals for conducting a search through the problem domain with the help of two main operations diffusion and updating. To improve the exploration and exploitation capability of SFSA, scale factor is used in place of random operator. The SFSA and proposed ISFSA is implemented and tested on six different multi objective complex test systems of power system. TOPSIS is used here as a decision making tool to find the best compromise solution between the two conflicting objectives. The outcomes of simulation results are also compared with recent reported methods to confirm the superiority and validation of proposed approach.

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Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Simhadri

Mohanty

2019

Int Trans Electr Energ Syst

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Summary In this work, a novel optimization technique named quasi‐oppositional whale optimization algorithm (QOWOA) is employed for the LFC problem. In this study to improve the convergence speed, accuracy, and reliability of WOA, WOA is integrated with quasi‐oppositional–based learning theory and applied first time for the LFC problem. To show the efficacy of the QOWOA technique, at first, it is applied to an interconnected two‐area system with governor dead band nonlinearity then expanded to a realistic three‐area hybrid power system, and another system with two‐area four‐unit hydro‐thermal system is considered. Proportional‐integral and dual‐mode PI controllers are considered as secondary controller for system analysis. The optimization supremacy of the QOWOA technique is validated contrasting the outcomes with original WOA, moth flame optimization algorithm, other recent competitive algorithms, and fuzzy PI controller for an identical system. Robustness of the projected approach is tested with random load perturbation at area‐1 and step load perturbation at diverse location at diverse instant that establishes the efficacy of QOWOA. The critical system analysis demonstrates that the proposed QOWOA technique may be effectively applied for the LFC problem.

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Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

Cited by 178 publications

References 41 publications

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Multi-Objective Optimal Dispatch Solution of Solar-Wind-Thermal System Using Improved Stochastic Fractal Search Algorithm

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

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