NDLI: UPMOA: An improved search algorithm to support user-preference multi-objective optimization

Content Provider	IEEE Xplore Digital Library
Author	Shuai Wang Ali, S. Tao Yue Liaaen, M.
Copyright Year	2015
Description	Author affiliation: Simula Res. Lab., Certus Software V&V Center, Oslo, Norway (Shuai Wang; Ali, S.; Tao Yue) \|\| Cisco Syst., Oslo, Norway (Liaaen, M.)
Abstract	Multi-objective search algorithms (e.g., non-dominated sorting genetic algorithm II (NSGA-II)) have been applied extensively to solve various multi-objective optimization problems in software engineering such as problems in testing. However, existing multi-objective algorithms usually treat all the objectives with equivalent priorities and do not provide a mechanism to reflect various user preferences when guiding search. The need to have such a mechanism was observed in one of our industrial projects on applying search algorithms for test optimization of a product line of Videoconferencing Systems (VCSs) called Saturn, where user preferences must be incorporated into optimization objectives, based on domain knowledge of test engineers for VCS testing. To address this, we propose an extension to the most commonly-used multi-objective search algorithm NSGA-II, which has shown promising results with user preferences. We name the extension as User-Preference Multi-Objective Optimization Algorithm (UPMOA), which includes a user preference indicator p and is based on existing weight assignment strategies. We empirically evaluated UPMOA with two industrial problems focusing on optimizing the test execution system for Saturn in Cisco. To assess the performance and scalability of UPMOA, inspired by the two industrial problems, in total we created 64000 artificial problems with 128 different sets of user preferences. The evaluation includes two aspects: 1) Three weight assignment strategies together with UPMOA were empirically evaluated to identify a best weight assignment strategy for p. Results show that the Uniformly Distributed Weights (UDW) strategy can assist UPMOA in achieving the best performance; 2) UPMOA was compared with three representative multi-objective search algorithms (including NSGA-II) and results show that UPMOA significantly outperformed the others and has the ability to solve problems with a wide range of complexity.
Starting Page	393
Ending Page	404
File Size	371912
Page Count	12
File Format	PDF
e-ISBN	9781509004065
DOI	10.1109/ISSRE.2015.7381833
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Search problems Optimization Testing Software algorithms Fault detection Genetic algorithms Software engineering Search Algorithms User-Preference Multi-objective Optimization Weight Assignment Strategy
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hybrid multi-objective genetic algorithm for multi-objective optimization problems

Software module clustering using a hyper-heuristic based multi-objective genetic algorithm

An improved multi-objective scatter search approach for solving selective disassembly optimization problem

An Improved Ranking Scheme for Selection of Parents in Multi-Objective Genetic Algorithm

Preference-based multi-objective software modelling

Multi-strategy Genetic Algorithm for Self-Configuring Solving of Complex Optimization Problems

An Improved Elitist Strategy Multi-Objective Evolutionary Algorithm

Hybridizing the Pareto Multi-Objective Optimization Evolutionary Algorithms by Means of Multi-Objective Local Search

A genetic algorithm and cell mapping hybrid method for multi-objective optimization problems

UPMOA: An improved search algorithm to support user-preference multi-objective optimization

Similar Documents

Hybrid multi-objective genetic algorithm for multi-objective optimization problems

Software module clustering using a hyper-heuristic based multi-objective genetic algorithm

An improved multi-objective scatter search approach for solving selective disassembly optimization problem

An Improved Ranking Scheme for Selection of Parents in Multi-Objective Genetic Algorithm

Preference-based multi-objective software modelling

Multi-strategy Genetic Algorithm for Self-Configuring Solving of Complex Optimization Problems

An Improved Elitist Strategy Multi-Objective Evolutionary Algorithm

Hybridizing the Pareto Multi-Objective Optimization Evolutionary Algorithms by Means of Multi-Objective Local Search

A genetic algorithm and cell mapping hybrid method for multi-objective optimization problems

UPMOA: An improved search algorithm to support user-preference multi-objective optimization