The integration of large-scale renewable energy and demand response (DR) resources in smart grids have brought in emerging challenges for transmission expansion planning (TEP), particularly in terms of system security...
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The integration of large-scale renewable energy and demand response (DR) resources in smart grids have brought in emerging challenges for transmission expansion planning (TEP), particularly in terms of system security. The conventional TEP models have not fully addressed the cost and the feasibility of corrective control (CC) actions such as generation rescheduling and load curtailment under contingencies. Moreover, the optimality of CC depends on the pre-contingency state, the post-contingency state, as well as the existence and viability of the involved CC actions. In this study, first the authors have given the explicit definition of CC risk index (CCRI), which evaluates the expected system performance under a set of contingencies (i.e. risk of incurring security issues). With the authors' improvement, the CCRI is now mathematically tractable and may have wide applications to TEP problems. Afterwards, the authors have proposed a multi-objective TEP framework with tradeoffs between cost and risk. A relatively new yet superior multi-objective evolutionaryalgorithm called the multi-objective evolutionaryalgorithm (MOEA)/D is introduced and employed to find Pareto optimal solutions. The proposed model is numerically verified on the modified IEEE RTS 24-bus and 118-bus systems. According to the simulation results, the proposed model can provide information regarding variants of risks and coordinate the optimum planning and DR solutions.
The aim of evolutionary multi/many-objective optimization is to obtain a set of Pareto-optimal solutions with good trade-off among the multiple conflicting objectives. However, the convergence and diversity of multiob...
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The aim of evolutionary multi/many-objective optimization is to obtain a set of Pareto-optimal solutions with good trade-off among the multiple conflicting objectives. However, the convergence and diversity of multiobjective evolutionaryalgorithms often seriously decrease with the number of objectives and decision variables increasing. In this paper, we present a decomposition-based evolutionary algorithm for solving scalable multi/many-objective problems. The key features of the algorithm include the following three aspects: (1) a resource allocation strategy to coordinate the utility value of subproblems for good coverage;(2) a multioperator and multiparameter strategy to improve adaptability and diversity of the population;and (3) a bidirectional local search strategy to prevent the decrease in exploration capability during the early stage and increase the exploitation capability during the later stage of the search process. The performance of the proposed algorithm is benchmarked extensively on a set of scalable multi/many-objective optimization problems. The statistical comparisons with seven state-of-the-art algorithms verify the efficacy and potential of the proposed algorithm for scalable multi/many-objective problems.
The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to i...
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The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionaryalgorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. Note to Practitioners-Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionaryalgorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problem
In order to exploit the enhancement of the multiobjective evolutionaryalgorithmbased on decomposition(MOEA/D), we propose an improved algorithm with uniform design(UD), i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD ...
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In order to exploit the enhancement of the multiobjective evolutionaryalgorithmbased on decomposition(MOEA/D), we propose an improved algorithm with uniform design(UD), i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the relationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEA/D with adaptive weight adjustment(MOEA/D-AWA), a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEA/D-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the differential evolution operator(MOEA/D-DE), MOEA/D-AWA and the nondominated sorting genetic algorithm II(NSGA-II) on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.
A number of weight vector-basedalgorithms have been proposed for many-objective optimization using the framework of MOEA/D (multi-objective evolutionaryalgorithmbased on decomposition). Those algorithms are charact...
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ISBN:
(纸本)9783319458236;9783319458229
A number of weight vector-basedalgorithms have been proposed for many-objective optimization using the framework of MOEA/D (multi-objective evolutionaryalgorithmbased on decomposition). Those algorithms are characterized by the use of uniformly distributed normalized weight vectors, which are also referred to as reference vectors, reference lines and search directions. Their common idea is to minimize the distance to the ideal point (i.e., convergence) and the distance to the reference line (i.e., uniformity). Each algorithm has its own mechanism for striking a convergence-uniformity balance. In the original MOEA/D with the PBI (penalty-based boundary intersection) function, this balance is handled by a penalty parameter. In this paper, we first discuss why an appropriate specification of the penalty parameter is difficult. Next we suggest a desired shape of contour lines of a scalarizing function in MOEA/D. Then we propose two ideas for modifying the PBI function. The proposed ideas generate piecewise linear and nonlinear contour lines. Finally we examine the effectiveness of the proposed ideas on the performance of MOEA/D for many-objective test problems.
In order to exploit the enhancement of the multi- objective evolutionaryalgorithmbased on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in ...
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In order to exploit the enhancement of the multi- objective evolutionaryalgorithmbased on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the re- lationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEND with adaptive weight ad- justment （MOEA/D-AWA）, a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEMD-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the dif- ferential evolution operator （MOEA/D-DE）, MOEA/D-AWA and the nondominated sorting genetic algorithm II （NSGA-II） on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.
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