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检索条件"主题词=Dynamic Multi-Objective Optimization"

共 185 条记录，以下是151-160 订阅

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Bicriterion Coevolution for the multi-objective Travelling Salesperson Problem

Bicriterion Coevolution for the Multi-objective Travelling S...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Liu, Ying Ying Thulasiraman, Parimala Pillay, Nelishia Univ Manitoba Dept Comp Sci Winnipeg MB Canada Univ Pretoria Dept Comp Sci Gauteng South Africa

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

The travelling salesperson problem is an NP-hard combinatorial optimization problem. In this paper, we consider the multi-objective travelling salesperson problem (MTSP), both static and dynamic, with conflicting objectives. NSGA-II and MOEA/D, two popular evolutionary multi-objective optimization algorithms suffer from loss of diversity and poor convergence when applied separately on MTSP. However, both these techniques have their individual strengths. NSGA-II maintains diversity through non-dominated sorting and crowding distance selection. MOEA/D is good at exploring extreme points on the Pareto front with faster convergence. In this paper, we adopt the bicriterion framework that exploits the strengths of Pareto-Criterion (PC) and Non-Pareto Criterion (NPC) evolutionary populations. In this research, NSGA-II (PC) and MOEA/D (NPC) coevolve to compensate the diversity of each other. We further improve the convergence using local search and a hybrid of order crossover and inver-over operators. To our knowledge, this is the first work that combines NSGA-II and MOEA/D in a bicriterion framework for solving MTSP, both static and dynamic. We perform various experiments on different MTSP benchmark datasets with and without traffic factors to study static and dynamic MTSP. Our proposed algorithm is compared against standard algorithms such as NSGA-II & III, MOEA/D, and a baseline divide and conquer coevolution technique using performance metrics such as inverted generational distance, hypervolume, and the spacing metric to concurrently quantify the convergence and diversity of our proposed algorithm. We also compare our results to datasets used in the literature and show that our proposed algorithm performs empirically better than compared algorithms.

关键词： multi-objective travelling salesperson problem dynamic multi-objective optimization combinatorial multi-objective optimization bicriterion coevolution local search genetic operator

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dynamic multi-objective evolutionary algorithm based on new model

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IMACS multiconference on Computational Engineering in Systems Applications (CESA 2006)

作者： Liu, Cbun-an Wang, Yuping Baoji Coll Arts & Sci Dept Math Baoji 721013 Peoples R China Xidian Univ Fac Sci Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9787302139225

This paper presents an new approach in which the rank and density of the individual are firstly defined and then the ideal variance of rank and density variance of population are clearly given. The ideal variance of rank is a measure of the quality of solutions, and the density variance is a measure of the uniformity of the distribution of solutions. Using these two measures as two objective functions, the multi-objective optimization problems is finally converted into a two objective optimization problem. For the transformed problem, a novel dynamic multiobjective evolutionary algorithm based on new model is proposed. In designing the algorithm, the uniform distribution index function is integrated into the mutation operator to adaptively adjust the search. As a result, the solutions will gradually move to the entire Pareto front and their distribution will gradually become uniform. The proposed approach is validated by using five benchmark functions taken from the standard literature on evolutionary multi-objective optimization. Results indicate the approach that is highly competitive and it can be considered a viable alternative to solve multi-objective optimization problems.

关键词： dynamic multi-objective optimization evolutionary algorithm uniform distribution U-measure

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Energy efficiency in high speed railway traffic operation: a real-time ecodriving algorithm 15

Energy efficiency in high speed railway traffic operation: a...

引用

15th IEEE International Conference on Environment and Electrical Engineering (EEEIC)

作者： Fernandez-Rodriguez, Adrian Fernandez-Cardador, Antonio Cucala, Asuncion P. Comillas Pontifical Univ Inst Res Technol IIT Madrid Spain

ISBN: (纸本)9781479979929

The application of ecodriving strategies leads to important energy savings in high speed railway systems. Ecodriving techniques are usually offline implemented in the design of commercial services. However, efficient driving can also be online recalculated to regulate the deviations from the schedule of the train. In this paper, a dynamic multi-objective model is proposed to calculate a set of efficient drivings that are updated during the journey. Using this model it is possible to react when delays arise replacing the current driving by a faster one. The new speed profile will be energy efficient under the new conditions. The model is solved using the extension of NSGA-II for dynamic optimization problems (DNSGA-II). This algorithm is combined with a detailed simulator of the train motion. The accuracy of the simulator ensures the fulfillment of the quality restrictions in commercial services. The proposed method has been applied to a case study using real data from a Spanish high speed line. The performance of DNSGA-II solving the dynamic model has been compared with NSGA-II. Furthermore, energy savings provided by the regulation algorithm have been evaluated comparing it with the typical driving style when a delay is detected.

关键词： DNSGA-II dynamic multi-objective optimization ecodriving energy saving high speed railway real time traffic operation simulation

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Surrogate Assisted Evolutionary Algorithm Based on Transfer Learning for dynamic Expensive multi-objective Optimisation Problems

Surrogate Assisted Evolutionary Algorithm Based on Transfer ...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Fan, Xuezhou Li, Ke Tan, Kay Chen City Univ Hong Kong Dept Comp Sci Tat Chee Ave Hong Kong Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England

ISBN: (纸本)9781728169293

dynamic multi-objective optimisation has attracted increasing attention in the evolutionary multi-objective optimisation community in recent years. Comparing to its static counterpart, which has been studied for more than half a century, the involvement of dynamic and uncertain features, including but not limited to the changing Pareto-optimal set, Pareto-optimal front and problem formulation, pose significant more challenges to evolutionary algorithms. This will become even more complicated when the underlying problem involves computationally expensive objective functions which are not rare in many real world application scenarios. In this paper, we pave an initial step towards the study of dynamic multi-objective optimisation with computationally expensive objective functions. More specifically, we use a surrogate assisted evolutionary algorithm, MOEA/D-EGO in particular, as the baseline in order to carry out evolutionary optimisation with a limited amount of function evaluations. Furthermore, instead of restart the MOEMD-EGO from scratch after each change, we use transfer learning to map the previously archived training data to the current landscape in order to jump start the surrogate model building process. By doing so, we can expect a better adaptation to the new environment. Proof-of-concept experiments fully demonstrate the effectiveness of our proposed method.

关键词： dynamic multi-objective optimization evolutionary algorithm surrogate modeling Gaussian process transfer learning

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dynamic multi-objective Home Appliance Scheduling Using Transfer Learning Based Evolutionary Algorithm 6

Dynamic Multi-Objective Home Appliance Scheduling Using Tran...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Zhang, Xi Jin, Yaochu Wu, Ping Liu, Qiqi Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Household Energy Management Systems (HEMS) have garnered significant public attention for their ability to optimize the operation of home appliances. Despite extensive research on optimal home appliance scheduling, these scheduling problems are inherently dynamic multi-objective optimization issues that can be effectively addressed using dynamic multi-objective evolutionary algorithms. This paper proposes a dynamic home appliance scheduling model built under time-of-use electricity tariffs to minimize energy costs and user dissatisfaction, taking into account varying user priorities for each appliance at different times. A hybrid method combining elitism-based transfer learning and diversity maintenance (HETD-RM-MEDA) is utilized to solve the scheduling problem, generating optimal schedules that balance energy costs and user dissatisfaction, and providing optimal on/off states for each appliance under different environments. Simulation studies demonstrate the feasibility and effectiveness of the proposed scheduling framework for home appliances under various scenarios.

关键词： dynamic multi-objective optimization evolutionary algorithm transfer learning home appliance scheduling

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dynamic Optimal Control of Sustained Overvoltage during Power System Restoration Process

Dynamic Optimal Control of Sustained Overvoltage during Powe...

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General Meeting of the IEEE-Power-and-Energy-Society

作者： Chen, Bin Wang, Hongtao Cao, Xi Shandong Univ Sch Elect Engn Jinan 250061 Shandong Peoples R China

ISBN: (纸本)9781467380409

Sustained power frequency overvoltage control strategy is an important part of power system restoration plan. The existing sustained over voltage control methods are based on static network, ignoring the dynamic restoration process. By choosing restoration targets as milestones, the dynamic process can be divided into several restoration sequences, each sequence consists of several restoration operations. In this paper, a dynamic multi-objective sustained overvoltage control model based on the restoration sequence is proposed, which takes the coupling of voltage control schemes at different stages into consideration. The objective functions adapted to the characteristics of restoration process are defined, including the operation risk of restoration sequence, adjusting time of voltage control scheme and voltage deviation of energized buses. The effectiveness of proposed model is verified by the simulation results of dynamic and static optimization under different restoration scenarios in Shandong power grid.

关键词： power system restoration sustained power frequency overvoltage control restoration sequence dynamic multi-objective optimization

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Reproducibility and Baseline Reporting for dynamic multi-objective Benchmark Problems

Reproducibility and Baseline Reporting for Dynamic Multi-obj...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Herring, Daniel Kirley, Michael Yao, Xin Univ Melbourne Melbourne Vic Australia Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9781450392372

dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purpose-built algorithms for DMOPs is often performed on narrow selections of dynamic instances with differing change magnitude and frequency or a limited selection of problems. In this paper, we focus on the reproducibility of simulation experiments for parameters of DMOPs. Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems. A baseline schema for dynamic algorithm evaluation is introduced, which provides a mechanism to interrogate performance and optimization behaviours of well-known evolutionary algorithms that were not designed specifically for DMOPs. Importantly, by determining the maximum capability of non-dynamic multi-objective evolutionary algorithms, we can establish the minimum capability required of purpose-built dynamic algorithms to be useful. The simplest modifications to manage dynamic changes introduce diversity. Allowing non-dynamic algorithms to incorporate mutated/random solutions after change events determines the improvement possible with minor algorithm modifications. Future expansion to include current dynamic algorithms will enable reproduction of their results and verification of their abilities and performance across DMOP benchmark space.

关键词： dynamic multi-objective optimization dynamic optimization dynamic benchmarks dynamic parameters dynamic frequency and severity

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Twisted Helical Antenna for Satellite-Mobile Handset Using dynamic multi-objective Self-adapting Differential Evolution Algorithm

Twisted Helical Antenna for Satellite-Mobile Handset Using D...

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6th International Symposium on Intelligence Computation and Applications (ISICA 2012)

作者： Zhang, Lian Zeng, Sanyou Liu, Zhu Gao, Steven Li, Zhengjun Jing, Hongyong China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Univ Surrey Surrey Space Ctr Guildford GU2 7XH Surrey England China Acad Space Technol Shaanxi 710000 Peoples R China

ISBN: (纸本)9783642342882

This paper presents a twisted helical antenna, which operates at 2.4GHz and is applied in satellite mobile communications. It is designed using dynamic multi-objective self-adaptive differential evolution (SaDE) algorithm which combines SaDE and dynamically controlling constraint technique. The main reason for adopting dynamic multi-objective SaDE in this paper is that few literatures have used dynamic multi-objective differential evolution (DE) for solving antenna optimization problem so far. A small twisted helical antenna optimized on a perfect ground plane. Results show that while our optimized antenna met the design requirements, its performance is not inferior to the quadriffilar helical antenna as seen in reference. The design shows the capability of evolution algorithm (EA) as an efficient optimization tool for searching globally optimal solutions for antenna design.

关键词： twisted helical antenna dynamic multi-objective optimization self-adaptive differential evolution evolutionary antenna

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Adaptive Intelligent Supplemental Light optimization based on Fluctuating Electricity Prices

Adaptive Intelligent Supplemental Light Optimization based o...

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IEEE Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS)

作者： Sorensen, Jan C. Reybroeck, Eva Univ Southern Denmark Campusvej 55 DK-5230 Odense M Denmark PCS Ornamental Plant Res Schaessestr 18 B-9070 Destelbergen Belgium

ISBN: (纸本)9798350366235;9798350366242

Growers cannot compromise product quality by saving energy, but increasing electricity prices pose an economic challenge. Greenhouse climate control systems must support advanced strategies to optimize these dynamic conflicting goals, but they do not currently do so. The approach utilizes existing hardware and integrates with local climate data, electricity price forecasts, and outdoor weather forecasts, enables advanced multiobjective optimization of climate parameters without requiring new hardware investments. In December 2022, researchers conducted a greenhouse experiment to evaluate the proposed system. Using dynamic multi-objective optimization, they were able to produce a variety of cultivars and save energy without sacrificing quality.

关键词： genetic algorithm dynamic multi-objective optimization software engineering greenhouse climate control

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A dynamic multi-objective evolutionary algorithm based on an orthogonal design

A dynamic multi-objective evolutionary algorithm based on an...

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IEEE Congress on Evolutionary Computation

作者： Zeng, Sang-you Chen, Guang Zheng, Liang Shi, Hui de Garis, Hugo Ding, Lixin Kang, Lishan China Univ Geosci Dept Comp Sci & Technol Wuhan 430074 Peoples R China Zhuzhou Inst Technol Dept Comp Sci & Technol Zhuzhou 412008 Peoples R China Wuhan Univ Int Sch Software Wuhan 430072 Peoples R China Wuhan Univ State Key Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9780780394872

There are rather few articles in the literature so far that deal with dynamic multi-objective optimization problems. This article introduces a dynamic orthogonal multi-objective evolutionary algorithm called "DOMOEA", that generalizes an earlier paper of ours (on an orthogonal multi-objective evolutionary algorithm (OMOEA-II) [1]) to dynamic environments. DOMOEA solves a particular class of dynamic multi-objective optimization problems, namely those that have continuous decision variables. This new algorithm uses the evolutionary results, before any environmental change, as the initial population after the environmental change. It applies an "orthogonal design method" to enhance the fitness of the population during the static stages between two successive changes of environment. We obtained satisfactory results when testing this algorithm against the benchmark problems proposed in the literature [4]. Our new algorithm is based on an ordinary evolutionary algorithm that does not have the capacity to detect environmental changes. Hence it has a comparatively simple structure, making comparisons with other dynamic multi-objective evolutionary algorithms relatively easy.

关键词： evolutionary algorithm orthogonal design dynamic multi-objective optimization Pareto optimal set (POS) Pareto optimal front (POF)

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