An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market

Jain, Pooja; Saxena, Akash

doi:10.1016/j.jestch.2019.03.005

Cited by 23 publications

(12 citation statements)

References 29 publications

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“…The opposition based MFO method is proposed in [34] to overcome the disadvantages of the conventional MFO which are trapping in local optima and the slow convergence. In [35] the conventional MFO is combined with lévy flights to gain their merits and to decrease the computational times, especially for the highly complex optimization problems.…”

Section: Nomenclature C Dgmentioning

confidence: 99%

Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

Elattar

Elsayed

2020

IEEE Access

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Due to the great impact of the penetration and locations of distributed generators (DG) on the performance of the distribution system, this paper proposes a modified moth flame optimization (MMFO) algorithm. Two modifications are proposed in MMFO to enhance the exploration and exploitation balance and overcome the shortcomings of the original MFO. The proposed MMFO is used to find the optimal location and sizing of DG units based on renewable energy sources in the distribution system. The main objective function is to minimize the total operating cost of the distribution system by considering the minimization of the total active power loss, voltage deviation of load buses, the DG units cost, and emission. This multi-objective function is converted to a coefficient single objective function with achieving different constraints. Also, the bus location index is employed to introduce the sorting list of locations to accomplish the narrow candidate buses list. Based on the candidate buses, the proposed MMFO is used to get the optimal location and sizing of DG units. The proposed MMFO algorithm has been applied to the IEEE 69-bus test distribution system and the results are compared with other published algorithms to prove its effectiveness and superiority.

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Section: Nomenclature C Dgmentioning

confidence: 99%

Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

Elattar

Elsayed

2020

IEEE Access

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“…the half population is random and the rest half is the opposite population of the same. This generalized OBL concept takes the random population and at the same time it also takes opposite population of generated random population and then total population search for an optimal solution, as a result, the solution comes from this optimization routine is nearer to the location of best optimal solution and the convergence progress becomes quicker.The steps involved in OBL concept indulge in initialization phase of algorithm are as follows: Initialization of the random population Y of size N/2. Calculate the opposite population

\overset{Y}{true}

as:

\overset{y_{i j}}{true}

u b_{i}

l b_{i}

−

y_{i j}

, i = 1, 2,…, N/2 and j = 1, 2,…,N/2. Combine the total population of size N from the set of these above mentioned two populations (i.e

\overset{Y}{true} \cup Y

). For population Y, the solution updation and their fitness function evaluation is done according to the basic FA1 and at the same time the opposite population

\overset{Y}{true}

is calculated and the fitness function for the same is also evaluated. In successive steps of FA2 algorithm the best N solutions from

\overset{Y}{true} \cup Y

based on their fitness functions is selected.…”

Section: A New Redefined Model Of Firefly Algorithmmentioning

confidence: 99%

“…The steps involved in OBL concept indulge in initialization phase of algorithm are as follows: Initialization of the random population Y of size N/2. Calculate the opposite population

\overset{Y}{true}

as:

\overset{y_{i j}}{true}

u b_{i}

l b_{i}

−

y_{i j}

, i = 1, 2,…, N/2 and j = 1, 2,…,N/2. Combine the total population of size N from the set of these above mentioned two populations (i.e

\overset{Y}{true} \cup Y

). …”

Section: A New Redefined Model Of Firefly Algorithmmentioning

confidence: 99%

A new redefined model of Firefly Algorithm with application to strategic bidding problem in Power Sector

Jain¹,

Saxena²

2020

Int Trans Electr Energ Syst

Self Cite

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Real‐life optimization problems require an effective mechanism that completely utilizes the search space to obtain optimal solutions. A designer has always an opportunity to propose a new effective solution technique to address this issue. In this order, this paper presents a new upgraded redefined model of nature‐inspired state of art meta‐heuristic firefly algorithm (FA). FA is centered on swarm intelligence, which is motivated by the flashing pattern and behavior of fireflies. However, for a few instances, FA has a tendency to trap in local optima and it exhibits slow convergence. The proposed model is enabled through Time‐varying Inertia Weight (TIW), Opposition Based Learning (OBL) and hybridized with sine cosine operators to get updated positions of search agents. A set of twenty‐two classical benchmark function problems with numerous range and features are employed to prove the efficacy of the proposed model. Also, the effects of the above‐mentioned modifications during the whole optimization routine are analyzed through different statistical and numerical analyses. The result analysis proves that the proposed modifications make FA more compatible. The proposed model is also tested on the strategic bidding problem of the power market of two different test systems with single and multi‐trading hours. All the reported results confirm the supremacy of the proposed redefined model of FA.

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“…Though, MOV suffers from slow searching, local minima, and premature convergence [63,48]. Parameter Three [17] Three [63] Three [51] Five [53] Three [16] Four [77] Complexity O(nlogn) [43] O(n.D.tmax) [57] O(m 2 ) [9] O(nm 2 ) [73] O(HM S × M + HM S × log(HM S)) [72] O(mn 2 ) [55] Convergence Smooth convergence with fast rate [43] Slow convergence rate [58] Fast convergence [74] Quickly converge [45] Suffer from premature convergence [24] Rapidly converged [77] Strength Balance between exploration and exploitation [30] Balance between intensification and diversification [3] Deal with the complex fitness landscape [29] Don't have overlapping and mutation calculation [5] Increases the diversity of the new solutions [13] Avoid trapped at local optimum [36] Weaknesses Relaxed convergence [31] Trapped in a local optimum [65] Evaluation is relatively expensive [78] Suffers from partial optimism [15] Get stuck on local optima [46] Needs huge memory resources [36] Therefore, there are several methods applied to solve these drawbacks including,the enhanced MVO proposed in [10], the authors improved the basic MVO by introducing a new version, called EMVO to achieve high accuracy and efficiency of the requirement prioritization. EMVO based on exchanging the information between the current solutions.…”

Section: Introductionmentioning

confidence: 99%

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

Shehab¹,

Abualigah

2021

Preprint

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Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

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An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market

Cited by 23 publications

References 29 publications

Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique

A new redefined model of Firefly Algorithm with application to strategic bidding problem in Power Sector

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

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