Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem

Xu, Xinlin; Hu, Zhongbo; Su, Qinghua; Xiong, Zenggang

doi:10.1155/2018/1027193

Cited by 15 publications

(11 citation statements)

References 39 publications

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“…In fact, there are many heuristic algorithms simulating the collective wisdom in different forms. e representative algorithms include collective decision optimization algorithm (CDOA) [70,71], teaching-learning-based optimization (TLBO) [72], learning backtracking search optimization algorithm (LBSA) [52], and so on. Unlike these algorithms, MBSAgC proposes the TOBL operator to deeply mine the winner-tendency of collective wisdom and designs an improved mutation operator to merge multiple learning strategies.…”

Section: Analysis and Discussion Of Mbsagcmentioning

confidence: 99%

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Liu

Miao³

et al. 2019

Mathematical Problems in Engineering

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The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.

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Section: Analysis and Discussion Of Mbsagcmentioning

confidence: 99%

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Liu

Miao³

et al. 2019

Mathematical Problems in Engineering

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“…The parameter settings which are used for optimizing the EEDP are the following: the size of population Npop = 200 individuals, the maximum number of generations q max = 100 and NFEs = 20,000, the crossover rate Pc = 0.97, and mutation rate P m = 0.09, respectively. The NS-SCGA was run 30 independent runs and it is compared with NPGA [9], a hybrid MO algorithm based on particle swarm optimization and differential evolution (MO-DE/PSO) [31], MOPSO with time variant inertia and acceleration coefficients (TV-MOPSO) [94], SPEA [11], multiobjective harmony search (MOHS) algorithm [95], NSGA-II, hybrid evolutionary algorithm (NSGA-II/EDA) [38], and Multiobjective Collective Decision Optimization Algorithm (MOCDOA) [96] in terms of their best solutions for both minimum emissions and minimum fuel cost (the extreme points on the Pareto front). Moreover, the comparison is also done in terms of the SP metrics.…”

Section: The Eedp: Case Studymentioning

confidence: 99%

A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems

Farag¹,

El-Shorbagy²,

Mousa³

et al. 2020

IJCIS

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The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. The performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. The operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDP. Finally, TOPSIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision-maker's preference.

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“…P max wt , P max pv and P max deg are maximum output power of wind turbine, photovoltaic and diesel generator, respectively. P c,max bat and P d,max bat are the maximum charge and discharge power by constraints in Equation (14). The SOC t is the state of charge at time interval t, P bat,t is the charging (or discharging) power at the time interval t, and C bat is the nominal capacity of the battery, as indicated by Equation (15).…”

Section: Constraintsmentioning

confidence: 99%

“…In addition, the authors in [10,11] present an optimization dispatch approach for microgrid operation in order to reduce the operation cost and improve environmental friendliness. The MOOD approach lets us weigh among several competing objective functions and explicitly consider the effect of different objective functions within microgrid operation [12][13][14]. However, the power loss from a large amount of energy conversion devices installed in the microgrid system has been ignored when solving the MOOD problem.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

Cao

Wang

et al. 2019

Energies

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With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

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Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem

Cited by 15 publications

References 39 publications

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

Deep‐Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom

A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems

A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

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