Evolutionary Algorithms With Segment-Based Search for Multiobjective Optimization Problems

Li, Miqing; Yang, Shengxiang; Li, Ké; Liu, Xiaohui

doi:10.1109/tcyb.2013.2282503

Cited by 38 publications

(15 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When optimising an MOP, the current nondominated solutions (i.e., the best solutions found so far) during the evolutionary process can indicate the evolutionary status [41,54]. The nondominated solution set, with the progress of the evolution, gradually approximates the Pareto front, thus being likely to reflect the shape of the Pareto front provided that it is well maintained.…”

Section: Basic Ideamentioning

confidence: 99%

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

Yao

2020

Evolutionary Computation

Self Cite

110

View full text Add to dashboard Cite

The quality of solution sets generated by decomposition-based evolutionary multiobjective optimisation (EMO) algorithms depends heavily on the consistency between a given problem's Pareto front shape and the specified weights' distribution. A set of weights distributed uniformly in a simplex often lead to a set of well-distributed solutions on a Pareto front with a simplex-like shape, but may fail on other Pareto front shapes. It is an open problem on how to specify a set of appropriate weights without the information of the problem's Pareto front beforehand. In this paper, we propose an approach to adapt the weights during the evolutionary process (called AdaW). AdaW progressively seeks a suitable distribution of weights for the given problem by elaborating five parts in the weight adaptation -weight generation, weight addition, weight deletion, archive maintenance, and weight update frequency. Experimental results have shown the effectiveness of the proposed approach. AdaW works well for Pareto fronts with very different shapes: 1) the simplex-like, 2) the inverted simplex-like, 3) the highly nonlinear, 4) the disconnect, 5) the degenerated, 6) the badly-scaled, and 7) the high-dimensional.

show abstract

Section: Basic Ideamentioning

confidence: 99%

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

Yao

2020

Evolutionary Computation

Self Cite

110

View full text Add to dashboard Cite

show abstract

“…Numeric comparing other HV with such a zero value is meaningless [43]. Thus, we consider this case as a failure for the corresponding algorithm, since its performance is significantly worse than those in this case.…”

Section: Performance Metricmentioning

confidence: 99%

Evolutionary Multi-Objective Workflow Scheduling in Cloud

Zhu

Zhang

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

Self Cite

347

170

View full text Add to dashboard Cite

Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required Quality of Service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an Evolutionary Multi-objective Optimization (EMO)-based algorithm to solve this workflow scheduling problem on an Infrastructure as a Service (IaaS) platform. Novel schemes for problemspecific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.

show abstract

“…When evolutionary algorithms (EAs) are applied in optimal models with multi-dimensional variables, the large search space may reduce search efficiency [30]. Moreover, an unreasonable evolutionary direction may yield local optimal solutions [31] instead of a globally optimal one Therefore, to better apply MOEAs to reservoir optimal operation models, a feasible search space that considers the characteristics of the IBWT project is proposed as an improved NSGA-II to solve the multi-objective model. The improved process can be described as follows:…”

Section: Feasible Search Spacementioning

confidence: 99%

Multi-Objective Optimal Operations Based on Improved NSGA-II for Hanjiang to Wei River Water Diversion Project, China

Bai

Huang

et al. 2019

Water

View full text Add to dashboard Cite

It is important to investigate the laws of reservoir multi-objective optimization operations, because it can obtain the best benefits from inter-basin water transfer projects to mitigate water shortage in intake areas. Given the multifaceted demands of the Hanjiang to Wei River Water Diversion Project, China (referred hereafter as “the Project”), an easy-to-operate multi-objective optimal model based on simulation is built and applied to search the multi-objective optimization operation rules between power generation and energy consumption. The Project includes two reservoirs connected by a water transfer tunnel. One is Huangjinxia, located in the mainstream of Hanjiang with abundant inflow but no regulation ability, and the other is Sanhekou, located in the tributary of Hanjiang with multi-year regulation ability but less water. The layout of the Project increases the difficulty of reservoir joint optimization operations. Therefore, an improved Non-dominated Sorting Genetic Algorithm-II (I-NSGA-II) with a feasible search space is proposed to solve the model based on long-term series data. The results show that: (1) The validated simulation model is helpful to obtain Pareto front curves to reveal the rules between power generation and energy consumption. (2) Choosing a reasonable search step size to build a feasible search space based on simulation results for the I-NSGA-II can help find more optimized solutions. Considering the influence of the initial populations of the algorithm and limited computing ability of computers, the qualified rate of Pareto points solved by I-NSGA-II are superior to NSGA-II. (3) According to the characteristics of the Project, water transfer ratio threshold value of two reservoirs are quantified for maximize economic benefits. Moreover, the flood season is a critical operation period for the Project, in which both reservoirs should supply more water to intake areas to ensure the energy balanced of the entire system. The findings provide an easy-to-operate multi-objective operation model with the I-NSGA-II that can easily be applied in optimal management of inter-basin water transfer projects by relevant authorities.

show abstract

Evolutionary Algorithms With Segment-Based Search for Multiobjective Optimization Problems

Cited by 38 publications

References 57 publications

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation

Evolutionary Multi-Objective Workflow Scheduling in Cloud

Multi-Objective Optimal Operations Based on Improved NSGA-II for Hanjiang to Wei River Water Diversion Project, China

Contact Info

Product

Resources

About