A Bi-Level Evolutionary Optimization for Coordinated Transmission Expansion Planning

Gupta, Neeraj; Khosravy, Mahdi; Patel, Nilesh; Senjyu, Tomonobu

doi:10.1109/access.2018.2867954

Cited by 59 publications

(20 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For this reason, we replace it by the closest value to 4 that belongs to D x1 which is 5 [5,11,16], respectively. Finally, the obtained solutions for the DSDM example are: (0,4,5), (0,9,11), (0,17,16), (5,4,5), (5,9,11), and (13,4,5). It is shown from Fig.…”

Section: Discussionmentioning

confidence: 98%

“…Fig. 13(b) illustrates the obtained results for the DSDM method with three decision variables where the domains are: [4,7,9,17], and D x3 = [5,8,11,16]. In a discrete space, the DSDM method needs a uniform spacing noted δ i in order to generate the coordinates of the reference points, this δ i is calculated for each decision variable as follows: δ i = max i /P (i is the decision variable index, P is a fixed parameter based on the dimension of the problem).…”

Section: Discussionmentioning

confidence: 99%

“…The two levels of the problem have their own objective(s), variable(s), and constraint(s). Several problems from the domain of transportation [1], logistics [2], engineering [3], industry [4], and economics [5] have inherent nested structure which needs them to be modeled as bi-level problems. In addition to complexity and increasing size of such problems, other difficulties could occur such as conflecting relationship between objectives of the two levels, non-linearity and/or multimodality in one or both levels, NP-hardness of BLOPs [6], deceptiveness of the lower-level fitness landscape, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

et al. 2020

View full text Add to dashboard Cite

Many decision making situations are characterized by a hierarchical structure where a lower-level (follower) optimization problem appears as a constraint of the upper-level (leader) one. Such kind of situations is usually modeled as a BLOP (Bi-Level Optimization Problem). The resolution of the latter usually has a heavy computational cost because the evaluation of a single upper-level solution requires finding its corresponding (near) optimal lower-level one. When several objectives are optimized in each level, the BLOP becomes a multi-objective task and more computationally costly as the optimum corresponds to a whole non-dominated solution set, called the PF (Pareto Front). Despite the considerable number of recent works in multi-objective evolutionary bi-level optimization, the number of methods that could be applied to the combinatorial (discrete) case is much reduced. Motivated by this observation, we propose in this paper an Indicator-Based version of our recently proposed Co-Evolutionary Migration-Based Algorithm (CEMBA), that we name IB-CEMBA, to solve combinatorial multi-objective BLOPs. The indicator-based search choice is justified by two arguments. On the one hand, it allows selecting the solution having the maximal marginal contribution in terms of the performance indicator from the lowerlevel PF. On the other hand, it encourages both convergence and diversity at the upper-level. The comparative experimental study reveals the outperformance of IB-CEMBA on a multi-objective bi-level productiondistribution problem. From the effectiveness viewpoint, the upper-level hyper-volume values and inverted generational distance ones vary in the intervals [0.8500, 0.9710] and [0.0072, 0.2420], respectively. From the efficiency viewpoint, IB-CEMBA has a good reduction rate of the Number of Function Evaluations (NFEs), lying in the interval [30.13%, 54.09%]. To further show the versatility of our algorithm, we have developed a case study in machine learning, and more specifically we have addressed the bi-level multiobjective feature construction problem.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Optimal parameters of all optimizer are adopted from their corresponding papers. [19][20][21][22][23][24][45][46][47][48][49][50] Each optimizer had a population size of 100 and run for 100000 function evaluations. 100 individual runs of each optimizer are carried out on each benchmark function to get comparative performance.…”

Section: Experimental Evaluation: Benchmarking Metomentioning

confidence: 99%

Mendelian Evolutionary Theory Optimization Algorithm

Gupta¹,

Khosravy²,

Patel³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

<div>This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.</div>

show abstract

“…the deep peak regulation of generators [4], emergency transmission rates of lines [5] and transmission switching [6]) are beneficial for expanding the feasible region of transmission sections. For power system planning, energy storage [7], flexible AC transmission systems (FACTS) [8], new transmission lines [9] are generally utilized to expand the feasible region of transmission sections and deliver more renewable energy. In this paper, we focus on expanding the feasible region of transmission sections through the power system planning.…”

mentioning

confidence: 99%

Feasible Region of Transmission Sections: Weak Factors Identification and Expansion

Chen¹,

Wen

Dong³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Feasible region limitation of transmission sections hinders the resource complementarity among regional power networks and the accommodation of renewable energies. To expand the feasible region of transmission sections, this paper proposes an optimization planning method with weak factors identification. In the method, the feasible region of transmission sections is characterized based on multiparametric programming considering the operational constraints of power systems. Then, based on the characterized feasible region, a weak factors identification method is proposed to capture the critical factors limiting the feasible region of transmission sections, such as specific transmission capacity constraints of lines and minimum output constraints of generators. Furthermore, an optimization planning model for upgrading the identified transmission lines and generators is constructed. With weak factors identification, the planning model can avoid traversing all transmission lines and generators and significantly reduce the computational burden. The effectiveness of the proposed method is demonstrated on IEEE 30-bus and 118bus systems.

show abstract

A Bi-Level Evolutionary Optimization for Coordinated Transmission Expansion Planning

Cited by 59 publications

References 37 publications

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

Mendelian Evolutionary Theory Optimization Algorithm

Feasible Region of Transmission Sections: Weak Factors Identification and Expansion

Contact Info

Product

Resources

About