Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators using Modified NSGA-II

M. Rajkumar,K. Mahadevan,S. Kannan,S. Baskar 대한전기학회 2013 Journal of Electrical Engineering & Technology Vol.8 No.3

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valvepoint loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

M. Rajkumar,K. Mahadevan,S. Kannan,S. Baskar 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.2

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

Application of Multi-Objective Genetic Algorithms to Multireservoir System Optimization in the Han River Basin

Taesoon Kim,Jun-Haeng Heo 대한토목학회 2006 KSCE journal of civil engineering Vol.10 No.5

The objective of multi reservoir system optimization is to achieve an optimal reservoir operating plan by the effective use of water resources. Many optimization techniques have been applied for the last decades, and researchers have recently interested in the heuristic approaches like evolutionary computation. This study proposes a methodology for applying multi-objective genetic algorithms (MOGAs) to a multireservoir system optimization in the Han River basin. The second generation evolutionary multi-objective technique, NSGA-Ⅱ, is used. The simulation model is applied to the Han River basin and the performance of the model is compared with the historical reservoir operation records. Two different cases are performed to evaluate the applicability of NSGA-Ⅱ. Case 1 shows the basic performance of NSGA-Ⅱ as applied to multireservoir system optimization, and Case 2 presents the methodology to discriminate the critical decision variables. In addition, the alternative releases and storages by NSGA-Ⅱ are compared with the historical releases and storages. Cases 1 and 2 show that NSGA-Ⅱ can be applied to multireservoir system optimization, and the alternative releases and storages computed using the results from NSGA-Ⅱ can be used as the possible reservoir operating plans that supply more water resources to downstream than the historical releases.

Combined Economic and Emission Dispatch with Valve-point loading of Thermal Generators using Modified NSGA-II

Rajkumar, M.,Mahadevan, K.,Kannan, S.,Baskar, S. The Korean Institute of Electrical Engineers 2013 Journal of Electrical Engineering & Technology Vol.8 No.3

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

NSGA-III Performance in Multi-objective Tour Guide Assignment Problem

Lina Setiyani,Takeo Okazaki 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.3

Optimization of a multi-objective tour guide assignment problem considering total guiding time, total assignment cost, and service quality is conducted. Several multi-objective evolutionary algorithms (MOEAs), such as the Non-dominated Sorting Genetic Algorithm III (NSGA-III), ε -NSGA-II, the epsilon MOEA (ε -MOEA), NSGA-II, the Pareto archived evolution strategy (PAES), and the Pareto Envelope-based Selection Algorithm II (PESA-II), have been used to solve and evaluate the problem in the MOEA framework. Based on the results, we found that NSGA-III gives better performance than the other algorithms in terms of solution quality and running time.

NSGA-II Technique for Multi-objective Generation Dispatch of Thermal Generators with Nonsmooth Fuel Cost Functions

Rajkumar, M.,Mahadevan, K.,Kannan, S.,Baskar, S. The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.2

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

다목적 PSO, NSGA-II 알고리즘을 활용한 DWAT 매개변수 최적화 평가

장원진 ( Wonjin Jang ),정지훈 ( Jeehun Chung ),김진욱 ( Jinuk Kim ),이용관 ( Yonggwan Lee ),김성준 ( Seongjoon Kim ) 한국농공학회 2023 한국농공학회 학술대회초록집 Vol.2023 No.0

본 연구에서는 Particle Swarm Optimization (PSO), Non-Dominated Sorting Genetic Algorithm II (NSGA-II)를 활용하여 The Dynamic Water Resources Assessment Tool (DWAT)의 자동 검보정 알고리즘을 개발하고 섬진강 유역을 대상으로 모형을 적용하여 이를 평가하고자 한다. 먼저, DWAT 수문모델은 소유역별로 지형학적 요인에 따른 유출 특성, 토양층에 따른 침투, 증발, 지하수 흐름 모의가 가능한 다양한 매개변수를 가지고 있어 유역별 적절한 매개변수 선정을 필요로 한다. 이를 위해 매개변수 최적화 알고리즘 모듈 개발을 위해 다목적 PSO와 NSGA-II 알고리즘을 활용하였다. PSO는 생체 군집의 사회적 행동양식을 바탕으로 설계된 군집 기반 확률론적 최적화 기법이며, NSGA-II는 다윈의 진화론을 모방하여 착안된 자연적인 선택과 진화를 기반으로 한 최적화 기법이다. 개발된 최적화 알고리즘은 Python으로 개발되어 확장성 및 범용성을 고려하였으며, 유역별 시공간적 특성을 고려할 수 있도록 사용자의 선택에 따라 9개의 목적함수(Coefficient of determination, (R²), modified R², Nash-Sutcliffe Efficiency (NSE), Modified NSE, Kling-Gupta Efficiency, Percent Bias, Root Mean Square Error, Ranked Sum of Squared Error, Chi-squared) 중 최대 3개까지 선택할 수 있게 작성되었다. PSO와 NSGA-II를 활용한 DWAT 모형 자동검보정 유출 해석은 다목적 함수와 표준유역별 유동적인 매개변수 산정을 통해 높은 모의 성능을 보여줄 것으로 판단된다.

NSGA-II 알고리즘을 이용한 다중 레이블 분류 문제에서 특징 선별

윤정훈(Jeonghun Yoon),이재성(Jaesung Lee),김대원(Dae-Won Kim) 한국정보과학회 2013 정보과학회논문지 : 소프트웨어 및 응용 Vol.40 No.3

최근 하나 이상의 클래스 레이블을 가지는 데이터에 대한 클래스 분류 기법들이 연구되고 있다. 그 중 몇몇 연구들에서 다중 레이블 분류의 성능을 높이기 위해 특징 선별 기법을 사용했다. 그러나 다중 레이블 데이터의 복잡성으로 인해 기존의 특징 선별 기법을 적용하는 것은 성능 향상에 한계가 있다. 본 논문은 다목적 최적화 알고리즘인 NSGA-II를 다중 레이블 특징 선별 문제에 활용했다. 또한 본 논문에서는 특징 선별 문제에 적합한 유전자 조작 기법을 제안하여 NSGA-II에 적용했다. 그리고 실제 다중 레이블 데이터 셋에서 실험을 통해 제안하는 알고리즘이 기존 유전자 알고리즘을 사용한 특징 선별 기법보다 더 좋은 성능을 가지는 것을 보인다. Recently, a lot of researchers are interested in multi-label classification. Some of the researchers use feature subset selection to improve performance in multi-label classification. However, because multi-label problem is more complex than single-label, single-label feature selection algorithms have limitation to be applied to multi-label data. In this paper, NSGA-II, which is multi-objective optimize algorithm, is used for multi-label feature selection. In addition, this study proposes a novel genetic operator for feature selection problem. Finally, experiments on real world data set show that proposed algorithm achieves better performance than traditional genetic algorithm.

설계 시간 가속화를 위한 NSGA-II 알고리즘 기반 LLC 공진형 컨버터용 고주파 변압기 최적 설계 기법

차명준,박수성,전선호,김래영 대한전기학회 2024 전기학회논문지 Vol.73 No.1

This paper proposes an optimal design method to minimize the volume and losses of the frequency converter for achieving high efficiency and high density in LLC resonant converters. Specifically, we focus on reducing the volume and losses of the high-frequency transformer, utilizing the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for this purpose. The proposed method, in contrast to global search methods exploring all possibilities, efficiently acquires Pareto Set values using NSGA-II, thereby reducing design time. The design process was conducted using Matlab, and the results obtained from applying the algorithm were compared with those from global search in terms of design time and accuracy. Additionally, the optimal design points were validated through experimentation.

Optimal Placement of SVC using NSGA-II

Shishir Dixit,Laxmi Srivastava,Ganga Agnihotri 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.9

Improving voltage stability, reducing real power loss (PL) and voltage deviation (VD) are the most important tasks in the operation of electrical power systems. Voltage instability and voltage collapse are the severe problems which may take place because of deficit reactive power at load buses due to increased loading or contingencies. In this paper, the problem of obtaining optimal location and size of SVC is formulated as true multi-objective optimization problem for simultaneous minimization of the two objectives namely real power losses and load bus voltage deviation. The two algorithms real coded genetic algorithm (RCGA) and non-dominated sorting genetic algorithm-II (NSGA-II) with a feature of adoptive crowding distance have been used for solving nonlinear constrained multi-objective optimization problem. Both the algorithms have been used for obtaining optimal location and sizing of SVC. Voltage security of the power system has also been analyzed separately for all placement of SVC to ensure secure operation of the system. The proposed approaches have been implemented on IEEE 30-bus test system. The simulation results of the two algorithms have been compared for solution quality, computational complexity and computational time. It has been found that NSGA-II presents better performance in solving multi-objective optimization problem and also in obtaining a diverse set of solutions which converge near the true Pareto-optimal front. The simulation results of NSGA-II have also been presented to exhibit the capabilities of the algorithm to generate well-distributed Pareto-optimal front.