To maximize the profits of both buyer and seller in renewable energy big data (REBD) transactions, a vector evaluated genetic algorithm (VEGA) based optimal trading strategy is proposed in this paper. Firstly, the REB...
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ISBN:
(纸本)9781538667750
To maximize the profits of both buyer and seller in renewable energy big data (REBD) transactions, a vector evaluated genetic algorithm (VEGA) based optimal trading strategy is proposed in this paper. Firstly, the REBD evaluation model is designed to calculate the objective function of VEGA. Multiple dimensions of REBD are used to set up the evaluation model. To address the problem in weight assignment of different aspects, analytic hierarchy process (AHP) based fuzzy evaluation method is adopted. Then, the problem of accessing win-win REBD transactions is converted into a multi-objective optimization problem. On this basis, vector evaluated genetic algorithm (VEGA) was used to solve the transaction optimization problem and find the Pareto solution by searching the non-inferior solution of all proposals. Finally, the accuracy of the proposed algorithm is verified by simulation results of a REBD transaction case.
An inverse analysis method that combines the back propagation neural network (BPNN) and vector evaluated genetic algorithm (VEGA) was proposed to identify mechanical geomaterial parameters for a more accurate predicti...
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An inverse analysis method that combines the back propagation neural network (BPNN) and vector evaluated genetic algorithm (VEGA) was proposed to identify mechanical geomaterial parameters for a more accurate prediction of deformation. The BPNN is used to replace the time-consuming numerical calculations, thus enhancing the efficiency of the inverse analysis. The VEGA is used to find the Pareto-optimal solutions to multiobjective functions. Unlike traditional back-analysis methods which are based on only 1 type of field measurement and a single objective function, this proposed method can consider multiple field observations simultaneously. The proposed method was applied to the Shapingba foundation pit excavation located in Chongqing city, China. Two types of measurements are considered in the method simultaneously: the displacements in the x-direction (north orientation) and those in the y-direction (east orientation). Five deformation modulus parameters for artificial backfill soil, silty clay, siltstone, sandstone, and mudstone were selected as the inversion parameters. Compared with the weighted sum approach, the proposed method was demonstrated as an efficient multi-objective optimization tool for back calculating undetermined parameters. After performing a forward-calculation using the optimized parameters obtained by the inverse analysis, the predicted results were well consistent with the practical deformation in magnitude and trend.
In this paper, a fast multi-objective hybrid evolutionary algorithm (MOHEA) is proposed to solve the bi-criteria flow shop scheduling problem with the objectives of minimizing makespan and total flow time. The propose...
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ISBN:
(纸本)9789811018374;9789811018367
In this paper, a fast multi-objective hybrid evolutionary algorithm (MOHEA) is proposed to solve the bi-criteria flow shop scheduling problem with the objectives of minimizing makespan and total flow time. The proposed algorithm improves the vector evaluated genetic algorithm (VEGA) by combing a new sampling strategy according to the Pareto dominating and dominated relationship-based fitness function. VEGA is good at searching the edge region of the Pareto front, but it has neglected the central area of the Pareto front, and the new sampling strategy prefers the center region of the Pareto front. The hybrid sampling strategy improves the convergence performance and the distribution performance. Simulation experiments on multi-objective test problems show that, compared with NSGA-II and SPEA2, the fast multi-objective hybrid evolutionary algorithm is better in the two aspects of convergence and distribution, and has obvious advantages in the efficiency.
This article deals with the coordination of security-constrained economic dispatch and load frequency control in an interconnected power system. The realistic and performance optimization inherent of the load frequenc...
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This article deals with the coordination of security-constrained economic dispatch and load frequency control in an interconnected power system. The realistic and performance optimization inherent of the load frequency control (LFC) and security-constrained economic dispatch are fully considered without simplifying assumptions. For this purpose, modeling security-constrained economic dispatch as a discontinuous control action in the continuous frequency response model of a power system is well addressed. Considering conflict behavior of LFC and security-constrained economic dispatch beside the powerfulness of the multi-objective geneticalgorithm (GA) to solve high-dimensional problems with conflicted objective functions makes it attractive for the automatic generation control coordination problem. The employed security-constrained economic dispatch utilizes the advantages of dynamic economic dispatch to achieve more realistic results. The GA is used to compute the decentralized control parameters and centralized generation levels of the on-line units to achieve an acceptable operating point. A significant modification in convergence speed has been performed by using LFC model properties in corporation with the geneticalgorithm, so the proposed method gives considerable promise for implementation in multi-area power systems. The efficiency of the proposed algorithm and modification is demonstrated on a three control area power system.
A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize yield and minimize risk in a given investment period. The selected assets in a portfolio not only collecti...
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ISBN:
(纸本)9781424413393
A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize yield and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its yield but also interactively define its risk as usually measured by a portfolio variance. In this paper we apply various techniques of multiobjective geneticalgorithms to solve portfolio optimization with some realistic constraints, namely cardinality constraints,, floor constraints and round-lot constraints. The algorithms experimented in this paper are vector evaluated genetic algorithm (VEGA), Fuzzy VEGA, Multiobjective Optimization geneticalgorithm (MOGA), Strength Pareto Evolutionary algorithm 2(nd) version (SPEA2) and Non-Dominated Sorting geneticalgorithm 2(nd) version (NSGA2). The results show that using fuzzy logic to combine optimization objectives of VEGA (in VEGA_Fuz1) for this problem does improve performances measured by Generation Distance (GD) defined by average distances of the last generation of population to the nearest members of the true Pareto front but its solutions tend to cluster around a few points. MOGA and SPEA2 use some diversification algorithms and they perform better in terms of finding diverse solutions around Pareto front. SPEA2 performs the best even for comparatively small number of generations. NSGA2 performs closed to that of SPEA2 in GD but poor in distribution.
The flight performance of a ramjet-powered missile is improved through the use of an automated optimization loop relying on computational-fluid-dynamics tools. A generic supersonic airbreathing missile is first descri...
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The flight performance of a ramjet-powered missile is improved through the use of an automated optimization loop relying on computational-fluid-dynamics tools. A generic supersonic airbreathing missile is first described, and its performance is assessed for a representative mission using Reynolds-averaged Navier-Stokes computations for aerodynamics prediction and theoretical engine performance models. The loop links together an optimization algorithm with an aerodynamic software computing the aerodynamic balance of the missile. Several optimizations are performed using different global algorithms such as simplex, evolutionary strategies, or geneticalgorithms. The first ones are mono-objective: for each point of the mission (acceleration, cruise, and maneuver), the best inlet shape has to be found. Then multiobjective optimizations are performed in order to find the pareto front, that is, the best set of shapes satisfying the whole mission.
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