A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem

Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.12th UK Workshop on Computational Intelligence (UKCI), Edinburgh, Scotland, 5-7 September 2012The course timetabling problem is a well known constraint optimization problem which has been of interest to researchers as well as practitioners. Due to the NP-hard nature of the problem, the traditional exact approaches might fail to find a solution even for a given instance. Hyper-heuristics which search the space of heuristics for high quality solutions are alternative methods that have been increasingly used in solving such problems. In this study, a curriculum based course timetabling problem at Yeditepe University is described. An improvement oriented heuristic selection strategy combined with a simulated annealing move acceptance as a hyper-heuristic utilizing a set of low level constraint oriented neighbourhood heuristics is investigated for solving this problem. The proposed hyper-heuristic was initially developed to handle a variety of problems in a particular domain with different properties considering the nature of the low level heuristics. On the other hand, a goal of hyper-heuristic development is to build methods which are general. Hence, the proposed hyper-heuristic is applied to six other problem domains and its performance is compared to different state-of-the-art hyper-heuristics to test its level of generality. The empirical results show that the proposed method is sufficiently general and powerful

Multi‐Objective Hyper‐Heuristics

Multi‐objective hyper‐heuristics is a search method or learning mechanism that operates over a fixed set of low‐level heuristics to solve multi‐objective optimization problems by controlling and combining the strengths of those heuristics. Although numerous papers on hyper‐heuristics have been published and several studies are still underway, most research has focused on single‐objective optimization. Work on hyper‐heuristics for multi‐objective optimization remains limited. This chapter draws attention to this area of research to help researchers and PhD students understand and reuse these methods. It also provides the basic concepts of multi‐objective optimization and hyper‐heuristics to facilitate a better understanding of the related research areas, in addition to exploring hyper‐heuristic methodologies that address multi‐objective optimization. Some design issues related to the development of hyper‐heuristic framework for multi‐objective optimization are discussed. The chapter concludes with a case study of multi‐objective selection hyper‐heuristics and its application on a real‐world problem

Effective Solution of University Course Timetabling using Particle Swarm Optimizer based Hyper Heuristic approach

عادة ما تكون مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) هي مشكلة تحسين الإندماجية. يستغرق الأمر جهود يدوية لعدة أيام للوصول إلى جدول زمني مفيد ، ولا تزال النتائج غير جيدة بما يكفي. تُستخدم طرق مختلفة من (الإرشاد أو الإرشاد المساعد) لحل UCTP بشكل مناسب. لكن هذه الأساليب عادةً ما تعطي حلول محدودة. يعالج إطار العمل الاسترشادي العالي هذه المشكلة المعقدة بشكل مناسب. يقترح هذا البحث استخدام محسن سرب الجسيمات استنادا على منهجية الإرشاد العالي (HH PSO) لمعالجة مشكلة الجدول الزمني للمحاضرات الجامعية (UCTP) . محسن سرب الجسيمات PSO يستخدام كطريقة ذات مستوى عالي لتحديد تسلسل الاستدلال ذي المستوى المنخفض (LLH) والذي من ناحية أخرى يستطيع توليد الحل الأمثل. لنهج المقترح يقسم الحل إلى مرحلتين (المرحلة الأولية ومرحلة التحسين). قمنا بتطوير LLH جديد يسمى "أقل عدد ممكن من الغرف المتبقية"  لجدولة الأحداث. يتم استخدام مجموعتي بيانات مسابقة الجدول الزمني الدولية (ITC)  ITC 2002 و ITC 2007 لتقييم الطريقة المقترحة. تشير النتائج الأولية  إلى أن الإرشاد منخفض المستوى المقترح يساعد في جدولة الأحداث في المرحلة الأولية. بالمقارنة مع LLH الأخرى ، الطريقة LLH المقترحة جدولت المزيد من الأحداث لـ 14 و 15 من حالات البيانات من 24 و 20 حالة بيانات من ITC 2002 و ITC 2007 ، على التوالي. تظهر الدراسة التجريبية أن HH PSO تحصل على معدل خرق أقل للقيود في سبع وستة حالات بيانات من ITC 2007 و ITC 2002 ، على التوالي. واستنتج هذا البحث أن LLH المقترحة يمكن أن تحصل على حل معقول وملائم إذا تم تحديد الأولوياتThe university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed approach generates solutions into two phases (initial and improvement). A new LLH named “least possible rooms left” has been developed and proposed to schedule events. Both datasets of international timetabling competition (ITC) i.e., ITC 2002 and ITC 2007 are used to evaluate the proposed method. Experimental results indicate that the proposed low-level heuristic helps to schedule events at the initial stage. When compared with other LLH’s, the proposed LLH schedule more events for 14 and 15 data instances out of 24 and 20 data instances of ITC 2002 and ITC 2007, respectively. The experimental study shows that HH PSO gets a lower soft constraint violation rate on seven and six data instances of ITC 2007 and ITC 2002, respectively. This research has concluded the proposed LLH can get a feasible solution if prioritized

Design Of Perturbative Hyper-Heuristics For Combinatorial Optimisation

Combinatorial optimisation is an area which seeks to identify optimal solution(s) from a discrete solution search space. Approaches for solving combinatorial optimisation problems can be separated into two main sub-classes, i.e. exact and approximation algorithms. Exact algorithm is a sub-class of techniques that is able to guarantee global optimality. However, exact algorithms are not feasible for solving complex problem due to its high computational overhead. Approximation algorithm is a sub-class of techniques which is able to provide sub-optimal solution(s) with reasonable computational cost. In order to explore the solution search space of a combinatorial optimisation problem, an approximation algorithm performs perturbations on the existing solutions by adopting a single or multiple perturbative Low-Level Heuristic(s) (LLHs). The use of a single LLH leads to poor performance when the particular heuristic is incompetent in solving the problem. Thus, the use of multiple LLHs is more desirable as the weaknesses of one heuristic can be compensated by the strengths of another. When there are multiple LLHs, a hyper-heuristic can be integrated to determine the choice of heuristics for a particular problem or situation. Hyper-heuristic automates the selection of LLHs through a high-level heuristic that consists of two key components, i.e. a heuristic selection method and a move acceptance method. The capability of a high-level heuristic is highly problem dependent as the landscape properties of a problem are unique among others. The high-level heuristics in the existing hyper-heuristics are designed by manually matching different combinations of high-level heuristic components

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

A case study of controlling crossover in a selection hyper-heuristic framework using the multidimensional knapsack problem

Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain. © 2016 Massachusetts Institute of Technolog

An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS),using the t -way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance

Solving high school timetabling problems worldwide using selection hyper-heuristics

High school timetabling is one of those recurring NP-hard real-world combinatorial optimisation problems that has to be dealt with by many educational institutions periodically, and so has been of interest to practitioners and researchers. Solving a high school timetabling problem requires scheduling of resources and events into time slots subject to a set of constraints. Recently, an international competition, referred to as ITC 2011 was organised to determine the state-of-the-art approach for high school timetabling. The problem instances, obtained from eight different countries across the world used in this competition became a benchmark for further research in the field. Selection hyper-heuristics are general-purpose improvement methodologies that control/mix a given set of low level heuristics during the search process. In this study, we evaluate the performance of a range of selection hyper-heuristics combining different reusable components for high school timetabling. The empirical results show the success of the approach which embeds an adaptive great-deluge move acceptance method on the ITC 2011 benchmark instances. This selection hyper-heuristic ranks the second among the previously proposed approaches including the ones competed at ITC 2011