Nasser R. Sabar

Grammatical Evolution Hyper-Heuristic for Combinatorial Optimization Problems

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains.

A harmony search algorithm for nurse rostering problems

Harmony Search Algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The Nurse Rostering Problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature.

A honey-bee mating optimization algorithm for educational timetabling problems

In this work, we propose a variant of the honey-bee mating optimization algorithm for solving educational timetabling problems. The honey-bee algorithm is a nature inspired algorithm which simulates the process of real honey-bees mating. The performance of the proposed algorithm is tested over two benchmark problems; exam (Carter’s un-capacitated datasets) and course (Socha datasets) timetabling problems. We chose these two datasets as they have been widely studied in the literature and we would also like to evaluate our algorithm across two different, yet related, domains. Results demonstrate that the performance of the honey-bee mating optimization algorithm is comparable with the results of other approaches in the scientific literature. Indeed, the proposed approach obtains best results compared with other approaches on some instances, indicating that the honey-bee mating optimization algorithm is a promising approach in solving educational timetabling problems.

Automatic design of a hyper-heuristic framework with gene expression programming for combinatorial optimization problems

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high-level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems, or even instances, have different landscape structures and complexity, the design of efficient high-level heuristics can have a dramatic impact on hyper-heuristic performance. In this paper, instead of using human knowledge to design the high-level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance-solving process, the high-level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low-level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high-level heuristics during the problem-solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism that contains a population of both high-quality and diverse solutions that is updated during the problem-solving process. The generality of the proposed hyper-heuristic is validated against six well-known combinatorial optimization problems, with very different landscapes, provided by the HyFlex software. Empirical results, comparing the proposed hyper-heuristic with state-of-the-art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains.

A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite.

Good Laboratory Practice for Optimization Research

Good Laboratory Practice has been a part of non-clinical research for over 40 years. Optimization Research, despite having many papers discussing standards being published over the same period of time, has yet to embrace standards that underpin its research. In this paper we argue the need to adopt standards in optimization research. Building on previous papers, many of which have suggested that the optimization research community should adopt certain standards, we suggest a concrete set of recommendations that the community should adopt. We also discuss how the proposals in this paper could be progressed.

Heterogeneous Cooperative Co-Evolution Memetic Differential Evolution Algorithm for Big Data Optimization Problems

Evolutionary algorithms (EAs) have recently been suggested as a candidate for solving big data optimization problems that involve a very large number of variables and need to be analyzed in a short period of time. However, EAs face a scalability issue when dealing with big data problems. Moreover, the performance of EAs critically hinges on the utilized parameter values and operator types, thus it is impossible to design a single EA that can outperform all others in every problem instance. To address these challenges, we propose a heterogeneous framework that integrates a cooperative co-evolution method with various types of memetic algorithms. We use the cooperative co-evolution method to split the big problem into subproblems in order to increase the efficiency of the solving process. The subproblems are then solved using various heterogeneous memetic algorithms. The proposed heterogeneous framework adaptively assigns, for each solution, different operators, parameter values and a local search algorithm to efficiently explore and exploit the search space of the given problem instance. The performance of the proposed algorithm is assessed using the Big Data 2015 competition benchmark problems that contain data with and without noise. Experimental results demonstrate that the proposed algorithm, with the cooperative co-evolution method, performs better than without the cooperative co-evolution method. Furthermore, it obtained very competitive results for all tested instances, if not better, when compared to other algorithms using lower computational times.

Meta-harmony search algorithm for the vehicle routing problem with time windows

It has been proven that the hybridization of the harmony search algorithm with a local search algorithm (LS) is essential to compensate for the inadequacy of its exploitation. However, the success of this hybridization relies on the achievement of a proper balance between HSA exploration and LS exploitation. The question is can we obtain this balance by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures? To address these issues, this work proposes a meta-harmony search algorithm (meta-HSA) that uses two HSA algorithms, an HSA-optimizer and HSA-solver. The HSA-optimizer will adaptively adjust the components and the configurations of the HSA-solver based on the search status. The HSA-solver, which is a hybridization of HSA and LS, takes the configuration generated by the HSA-optimizer as input and then solves the given problem instance. That is, the HSA-optimizer operates on the components and the configurations of the HSA-solver, while the HSA-solver operates directly on the given problem instance (the solution search space). The proposed meta-HSA was applied to Solomons vehicle routing problem with time windows benchmark to verify its effectiveness compared with standard HSA and the state-of-the art methods. The results of the comparison confirmed that the meta-HSA produces competitive results with respect to the other methods. Therefore, we can conclude that the meta-optimization technique does assist the hybrid HSA in obtaining the appropriate selection of its components and configurations during the search process. This demonstrates that the meta-HSA can provide a proper balance between exploration and exploitation by adaptively selecting (i) HSA parameter values, (ii) LS and its parameters and (iii) the LS neighborhood structures. Moreover, the meta-HSA optimizer decreases the effort exerted by the user in tuning these components and configurations.

Tabu exponential Monte-Carlo with counter heuristic for examination timetabling

In this work, we introduce a new heuristic TEMCQ (Tabu Exponential Monte-Carlo with Counter) for solving exam timetabling problems. This work, an extension of the EMCQ (Exponential Monte-Carlo with Counter) heuristic that was originally introduced by Ayob and Kendall. EMCQ accepts an improved solution but intelligently accepts worse solutions depending on the solution quality, search time and the number of consecutive non-improving iterations. In order to enhance the EMCQ heuristic, we hybridise it with tabu search, in which the accepted moves are kept in a tabu list for a certain number of iterations in order to avoid cyclic moves. In this work, we test TEMCQ on the un-capacitated Carters benchmark examination timetable dataset and evaluate the heuristic performance using standard proximity cost. We compare our results against other methodologies that have been reported in the literature over recent years. Results demonstrate that TEMCQ produces good results and outperforms other approaches on several benchmark instances.

Roulette Wheel Graph Colouring for Solving Examination Timetabling Problems

This work presents a simple graph based heuristic that employs a roulette wheel selection mechanism for solving exam timetabling problems. We arrange exams in non-increasing order of the number of conflicts (degree) that they have with other exams. The difficulty of each exam to be scheduled is estimated based on the degree of exams in conflict. The degree determines the size of a segment in a roulette wheel, with a larger degree giving a larger segment. The roulette wheel selection mechanism selects an exam if the generated random number falls within the exams segment. This overcomes the problem of repeatedly choosing and scheduling the same sequence of exams. We utilise the proposed Roulette Wheel Graph Colouring heuristic on the un-capacitated Carters benchmark datasets. Results showed that this simple heuristic is capable of producing feasible solutions for all 13 instances.