مشاعل سليمان معشي

مجال التميز

تميز دراسي و بحثي

البحوث المنشورة

البحث (1):

عنوان البحث:

A Multi-objective Hyper-heuristic based on Choice Function

رابط إلى البحث:

Click here

تاريخ النشر:

26/01/2014

موجز عن البحث:

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. There are different types of hyper-heuristic methodologies and in this study, we present a learning selection hyper-heuristic to solve multi-objective optimization problems. This high level methodology controls and combines the strengths of three well-known multiobjective evolutionary algorithms (NSGAII, SPEA2, and MOGA) as the low level heuristics. The performance of the proposed approach is compared to the performance of each low level heuristic run on its own as well as other approaches including an adaptive multi-method search, namely AMALGAM. The experimental results demonstrate the effectiveness of the hyper-heuristic approach over the Walking Fish Group test suite, a common benchmark for multi-objective optimization.

البحث (2):

عنوان البحث:

Choice function based hyper-heuristics for multi-objective optimization

رابط إلى البحث:

Click here

تاريخ النشر:

 December 2014

موجز عن البحث:

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic.

المؤتمرات العلمية:

المؤتمر (1):

عنوان المؤتمر:

The 7th Saudi Students Scientific Conference

تاريخ الإنعقاد:

01/02/2014

بلد ومكان الإنعقاد:

Edinburgh - Scotland, UK

طبيعة المشاركة:

Presentation

عنوان المشاركة:

Comparison of Multi-objective Hyper-heuristics on tri-objective WFG test problems

موجز عن المشاركة:

In this study, the performance of three multi-objective selection choice function based hyper-heuristics that are combined with different move acceptance strategies including all-moves (AM), great deluge algorithm (GDA) and late acceptance (LA) are evaluated on the tri-objective the Walking Fish Group (WFG) test problems, which is as a common benchmark for multi-objective optimisation. The performance of our hyper-heuristics are compared to the well established multi-objective evolutionary algorithm; SPEA2. The experimental results demonstrate the effectiveness of the multi-objective choice function great deluge based hyper-heuristic.