Adaptive geometric angle-based algorithm for pruning pareto-optimal sets

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Sudeng S., Wattanapongsakorn N.

ผู้เผยแพร่: Brno University of Technology

ปีที่เผยแพร่ (ค.ศ.): 2013

หน้าแรก: 75

หน้าสุดท้าย: 80

จำนวนหน้า: 6

ISBN: 9788021447554

นอก: 1803-3814

eISSN: 1803-3814

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905732532&partnerID=40&md5=c68464f8ff303faa66b8f22c663c3e81

ภาษา: English-Great Britain (EN-GB)

บทคัดย่อ

Most of multi-objective evolutionary algorithms (MOEAs) approximate Pareto-optimal solutions covering wide area of the whole Pareto front favoring diversity property. The decision maker (DM) still works hard to compromise the large trade-off solutions. In this paper, we propose an algorithm to help the DM choosing the final best solution based on his/her preference. The main contribution of our algorithm is to filter out undesired solutions and provide more robust trade-off set of optimal solutions to the DM. Our algorithm is called an adaptive angle based pruning algorithm (ADA). The pruning rationale is to expand the dominated area for the purpose of removing solutions that only marginally improves in some objectives while being significantly worse in other objectives. Our pruning method begins by calculating the angle between a pair of solutions by using arctangent function. The bias intensity parameter is introduced as a minimum threshold angle in order to approximate the portions of desirable solutions based on the DM's preference. Initially, we consider several benchmark problems by applying a simple version of MOEA/D algorithm. Then, we apply the pruning algorithm. The experimental results have shown that our pruning algorithm provides robust sub-set of Pareto-optimal solutions for the benchmark problems. The pruned Pareto-optimal solutions distribute and cover multiple regions of Pareto front even when the strongest bias is applied. In addition, it is clearly shown in bi-objective problems that the pruned Pareto-optimal solutions are located at knee regions of the Pareto front.

คำสำคัญ

Adaptive angle-based pruning algorithm, Decision making analysis, Pareto-optimal solutions, Preference articulation techniques