Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem

Abido, M. A.

doi:10.1007/978-3-642-01799-5_3

Cited by 114 publications

(173 citation statements)

References 41 publications

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“…The most widely used new offspring creating mechanism is GA's genetic operators [1,19,38,41,59]. In recent years, some new strategies were reported to improve the convergence speed of EAs, which is subject to high success rate of finding the true POS.…”

Section: Discussion Of Convergence Speed In Dmomentioning

confidence: 99%

“…The early works have been summarized in the books [15,16]. Currently, the widely used sMOEAs in engineering applications [1,25,38] include nondominated sorting genetic algorithm II (NSGA-II) [19], strength Pareto evolutionary algorithm II [59] and Pareto develop based selection algorithm [33]. For many sMO tasks, these approaches have shown excellent performances.…”

Section: Related Work and Necessity Of Developing Dmoeasmentioning

confidence: 99%

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Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Wang

2009

Memetic Comp.

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Dynamic optimization and multi-objective optimization have separately gained increasing attention from the research community during the last decade. However, few studies have been reported on dynamic multi-objective optimization (dMO) and scarce effective dMO methods have been proposed. In this paper, we fulfill these gabs by developing new dMO test problems and new effective dMO algorithm. In the newly designed dMO problems, Pareto-optimal decision values (i.e., Pareto-optimal solutions: POS) or both POS and Pareto-optimal objective values (i.e., Pareto-optimal front: POF) change with time. A new multi-strategy ensemble multi-objective evolutionary algorithm (MS-MOEA) is proposed to tackle the challenges of dMO. In MS-MOEA, the convergence speed is accelerated by the new offspring creating mechanism powered by adaptive genetic and differential operators (GDM); a Gaussian mutation operator is employed to cope with premature convergence; a memory like strategy is proposed to achieve better starting population when a change takes place. In order to show the advantages of the proposed algorithm, we experimentally compare MS-MOEA with several algorithms equipped with traditional restart strategy. It is suggested that such a multi-strategy ensemble approach is promising for dealing with dMO problems.

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Section: Discussion Of Convergence Speed In Dmomentioning

confidence: 99%

Section: Related Work and Necessity Of Developing Dmoeasmentioning

confidence: 99%

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Wang

2009

Memetic Comp.

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“…In general, the power plants want to reduce the cost involved in power generation, often neglecting the amount of pollutants they produce. Due to increase in awareness over the environmental pollution (Talaq et al 1994;Abido 2006), there is an increase in need that demands the pollution to be controlled and to be reduced. That is, the EELD problem is to balance the two conflicting objectives of minimizing fuel cost and emission cost.…”

Section: Economic Emission Load Dispatchmentioning

confidence: 99%

Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Bhuvana¹,

Aravindan²

2015

Soft Comput

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Evolutionary multi-objective optimization algorithms are generally employed to generate Pareto optimal solutions by exploring the search space. To enhance the performance, exploration by global search can be complemented with exploitation by combining it with local search. In this paper, we address the issues in integrating local search with global search such as: how to select individuals for local search; how deep the local search is performed; how to combine multiple objectives into single objective for local search. We introduce a Preferential Local Search mechanism to fine tune the global optimal solutions further and an adaptive weight mechanism for combining multi-objectives together. These ideas have been integrated into NSGA-II to arrive at a new memetic algorithm for solving multi-objective optimization problems. The proposed algorithm has been applied on a set of constrained and unconstrained multi-objective benchmark test suite. The performance was analyzed by computing different metrics such as Generational distance, Spread, Max spread, and HyperVolume Ratio for the test suite functions. Statistical test applied on the results obtained suggests that the proposed algorithm outperforms the state-of-art multiobjective algorithms like NSGA-II and SPEA2. To study the performance of our algorithm on a real-world application, Economic Emission Load Dispatch was also taken up for validation. The performance was studied with the help of measures such as Hypervolume and Set Coverage Metrics. Experimental results substantiate that our algorithm has Communicated by V. Loia.

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“…These emissions can be modeled through functions that associate emissions with power production for each unit. One approach to represent SO 2 and NO x emissions is to use a combination of polynomial and exponential terms [21]: The bi-objective combined economic emission dispatch problem is converted into a single optimization problem by introducing the price penalty factor h as follows,…”

Section: Total Emission Cost Minimizationmentioning

confidence: 99%

A new technique for solving the multi-objective optimization problem using hybrid approach

2014

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Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO 2 emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multiobjective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.

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Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem

Cited by 114 publications

References 41 publications

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

A new technique for solving the multi-objective optimization problem using hybrid approach

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