Hyper-heuristic Algorithm Research Articles

A multi-objective evolutionary hyper-heuristic algorithm for team-orienteering problem with time windows regarding rescue applications

Abstract The team-orienteering problem (TOP) has broad applicability. Examples of possible uses are in factory and automation settings, robot sports teams, and urban search and rescue applications. We chose the rescue domain as a guiding example throughout this paper. Hence, this paper explores a practical variant of TOP with time window (TOPTW) for rescue applications by humanoid robots called TOPTWR. Due to the significant range of algorithm choices and their parameters tuning challenges, the use of hyper-heuristics is recommended. Hyper-heuristics can select, order, or generate different low-level heuristics with different optimization algorithms. In this paper, first, a general multi-objective (MO) solution is defined, with five objectives for TOPTWR. Then a robust and efficient MO and evolutionary hyper-heuristic algorithm for TOPTW based on the humanoid robot’s characteristics in the rescue applications (MOHH-TOPTWR) is proposed. MOHH-TOPTWR includes two MO evolutionary metaheuristics algorithms (MOEAs) known as non-dominated sorting genetic algorithm (NSGA-III) and MOEA based on decomposition (MOEA/D). In this paper, new benchmark instances are proposed for rescue applications using the existing ones for TOPTW. The experimental results show that MOHH-TOPTWR in both MOEAs can outperform all the state-of-the-art algorithms as well as NSGA-III and MOEA/D MOEAs.

Automated Course Timetabling Optimization Using Tabu-Variable Neighborhood Search Based Hyper-Heuristic Algorithm

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers’ requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works.

Parallel Hyper-Heuristic Algorithm for Multi-Objective Route Planning in a Smart City

Most of the commercial navigation products provide route planning service for users. However, they only consider a single metric such as distance, time, or other costs, while ignoring a critical criterion: safety. In a smart city, people may prefer to find a safe walking route to avoid the potential crime risk as well as obtain a short distance. This problem can be specified as a multi-objective optimization problem (MOOP). Many methods were proposed in the past to solve the multi-objective route planning, the multi-objective evolutionary approach (MOEA) is considered as the most popular one. However, MOEA is non-optimized when used in a large-scale road network and becomes computationally expensive when handling a large population size. In this paper, we propose a multi-objective hyper-heuristic (MOHH) framework for walking route planning in a smart city. In the search framework, we design a set of low level heuristics to generate new routes. Moreover, we adopt reinforcement learning mechanism to select good low-level heuristics to accelerate searching speed. We further improve the reinforcement learning-based multi-objective hyper-heuristic (RL-MOHH) algorithm and implement a parallel version (RL-PMOHH) on general purpose graphic process unit. Extensive experiments are conducted on the safety-index map constructed from the historical urban data of the New York city. Comprehensive experimental results show that the proposed RL-PMOHH is almost 173, 5.3, and 3.1 times faster than the exact multi-objective optimization algorithm, the RL-MOHH algorithm, and the parallel NSGA-II algorithm, respectively. Moreover, both RL-MOHH and RL-PMOHH can obtain more than 80% Pareto optimal solutions in a large-scale road network.

Parallel Hyperheuristic Algorithm for the Design of Pipeline Networks

A hyperheuristic optimization technique to reduce computational times for the design of pipeline networks is presented. The proposed strategy is an A-team approach comprising the guided execution of three metaheuristics: a genetic algorithm, simulated annealing, and an ant colony optimization. Besides, a specialized learning mechanism for information exchange was defined in order to speed up the search process. Moreover, the algorithm was implemented in parallel so as to allow several metaheuristics to run simultaneously, thus achieving a significant reduction of time overhead. In the algorithmic design, realistic scenarios were employed so as to appraise the impact of each agent on optimization efficiency. The cases correspond to real-world offshore infrastructures to be located in the Argentinian marine platform. They were also analyzed to illustrate the validity and suitability of the proposed approach. This optimization technique proved to be competitive since it is able to explore a wide search space...

Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm

For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our proposed technique test cases are optimized by utilizing Improved Cuckoo Algorithm (ICSA). At that point, the advanced experiments are organized or prioritized by utilizing Particle Swarm Optimization algorithm (PSO). The Particle Swarm Optimization and Improved Cuckoo Algorithm (PSOICSA) estimation is a blend of Improved Cuckoo Search Algorithm(ICSA) and Particle Swarm Optimization (PSO). PSOICSA could be utilized to advance the test suite, and coordinate both ICSA and PSO for a superior outcome, when contrasted with their individual execution as far as experiment improvement.

A Research on Multi-Project Human Resource Scheduling in R & D Enterprises——A Hyper-Heuristic Algorithm Based on Ant Colony Optimization

A Research on Multi-Project Human Resource Scheduling in R & D Enterprises——A Hyper-Heuristic Algorithm Based on Ant Colony Optimization

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper, we propose a heuristic- and a machine learning (ML)-based VM allocation method and compare them in terms of energy, quality of service (QoS), network traffic, migrations, and scalability for various DC scenarios. Then, we present a novel hyper-heuristic algorithm that exploits the benefits of both methods by dynamically finding the best algorithm, according to a user-defined metric. For optimality assessment, we formulate an integer linear programming (ILP)-based VM allocation method to minimize energy consumption and data communication, which obtains optimal results, but is impractical at runtime. Our results demonstrate that the ML approach provides up to 24% server-to-server network traffic improvement and reduces execution time by up to $480{\times }$ compared to conventional approaches, for large-scale scenarios. On the contrary, the heuristic outperforms the ML method in terms of energy and network traffic for reduced scenarios. We also show that the heuristic and ML approaches have up to 6% energy consumption overhead compared to ILP-based optimal solution. Our hyper-heuristic integrates the strengths of both the heuristic and the ML methods by selecting the best one during runtime.

ALGORITHM OF STEPS MINIMIZATION FOR ORTHOGONAL FIGURE CUTTING

The problem of orthogonal cutting of complex figures defined by multi- ply connected orthogonal polygons is considered. The urgency of this task is due to its broad practical interpretation, as well as its belonging to the NP-hard class. As exact methods lose their effectiveness in solving NP-hard problems, because costs increase up to unacceptable values, various heuristic and hyper-heuristic algorithms are developed for solv- ing orthogonal figured cutting. The problem of figured cutting is often found in practice in resource-intensive industries, where the optimiza- tion of the process of raw materials consumption plays an important economic role. In particular, the problem of cutting multiply connected orthogonal polygons from a sheet resource has to be solved daily in the construction industry, at enterprises when cutting cardboard packages, and when making furniture. The complexity of the problem lies in the presence of the cut-off blanks internal voids, which often in practice are quite large and are able to accommodate other cut-off blanks. The desire to optimize the packaging process leads to the emergence of a new math- ematical model and specific limitations, while the algorithmization of any methods associated with the solution of the task is significantly complicated compared to the classical rectangular nesting. In this paper, a meaningful statement of the problem is described, its mathematical model is given. The review of algorithms described in the literature and developed earlier, allowing to obtain rational plans for cut- ting the material in the case of pattern cutting is made. The algorithm described in the article is based on a number of heuristic ideas, its main idea is to reduce the so-called gradation of the cutting map. The gradation here is defined as the amplitude of the height difference of the contour of the cutting pattern of the blanks. The computational time of the algorithm when using the selected heuristic ideas is acceptable for its use in practice when solving real problems of resource-intensive industries.

Improved RMS delay and optimal system design of LED based indoor mobile visible light communication system

The light emitting diode (LED) based lighting systems can be used for creation of the indoor communication network for sending information besides serving its main purpose of illumination. However, there are a number of impediments which are still under resolution in order to realize the full potential of such a system. In particular, the visible light communication (VLC) systems suffer due to high inter symbol interference (ISI) mainly on account of multi path propagation which impacts the spectral efficiency of the system. Besides ensuring ubiquitous coverage, it is also important to improve the system’s bandwidth within the indoor scenario. The optimal deployment of such systems may result in optimum resource utilization (LEDs and driver circuits etc.) to minimize the energy consumption and to achieve improved operational efficiency. In this paper, we propose two types of LED deployment strategies — centralized and distributed and compare their performances on the basis of average outage area ratio, effect of semi-angle, root mean square delay and data transmission rate. The hyper heuristic evolutionary algorithm (HypEA) has been implemented to optimize the performance of the systems to achieve full receiver mobility in the indoor environment. The experimental results show that the distributed deployment strategy is able to optimize the system performance significantly in comparison to centralized deployment strategy.

Unified multi‐objective mapping for network‐on‐chip using genetic‐based hyper‐heuristic algorithms

In this study, a flexible energy- and delay-aware mapping approach is proposed for the co-optimisation of energy consumption and communication latency for network-on-chips (NoCs). A novel genetic-based hyper-heuristic algorithm (GHA) is proposed as the core algorithm. This algorithm consists of bottom-level optimisation which includes a variety of operators and top-level optimisation which selects suitable operators through a ‘reward’ mechanism. As this algorithm can select suitable operators automatically during the mapping process, it noticeably improves convergence speed and demonstrates excellent stability. Compared to the random algorithm, GHA can achieve on average 23.28% delay reduction and 11.81% power reduction. Compared to state-of-the-art mapping algorithms, GHA produces improved mapping results with less time, especially when the size of NoC is large.

HTSS: a hyper-heuristic training set selection method for imbalanced data sets

Imbalanced data sets are those in which data samples have uneven distribution amongst the classes. When classifying such data, classical classifiers encounter problem; hence, this problem has become a challenging issue in the field of machine learning. To weaken this problem, we propose a novel hyper-heuristic algorithm, called HTSS, to select the best training samples in this paper. In other words, the best training sample subset is chosen with the goal of enhancing the performance of classifier when confronting imbalanced data. To do so, some local search algorithms and a choice function are incorporated with a global search algorithm to improve its effectiveness. The global search used in this paper is binary quantum inspired gravitational search algorithm (BQIGSA) which is a recently proposed meta-heuristic search for optimization of binary encoded problems. Experiments are performed on 75 imbalanced data sets, and G-mean and AUC measures are employed for evaluation. The results of comparing the proposed method with other state of the art algorithms show the superiority of the proposed HTSS method.

Hyperheuristic genetic algorithm for steelmaking continuous casting rescheduling based on strong disturbance of task

The problem of steelmaking and continuous casting rescheduling is the key problem in the steel production scheduling. Aiming at the problem of steelmaking and continuous casting rescheduling under strong disturbance of task in this paper, a fast and efficient hyperheuristic genetic algorithm is proposed to minimise the difference degree before and after the scheduling adjustment as the objective function. In the framework of the hyperheuristic algorithm, the high layer strategy is designed as a self-learning heuristic rule selection strategy, the low layer design is a series of genetic operators related to steelmaking and continuous casting scheduling, optimal solution of update iteration by searching for each other through high and low two layers of strategy in the effective threshold. Simulation experiments show that the effectiveness of the algorithm can meet the needs of real-time and stable production to the maximum extent.

Hyperheuristic genetic algorithm for steelmaking continuous casting rescheduling based on strong disturbance of task

Hyperheuristic genetic algorithm for steelmaking continuous casting rescheduling based on strong disturbance of task

Hyper-heuristic for steelmaking casting rescheduling based on strong disturbance

The stochastic disturbance in steelmaking casting often leads to failure of the original production plan. Rescheduling is necessary for the steel manufacturing system. In the background of the increasing scale of scheduling and the numerous kinds of disturbance, the different algorithms are designed for different rescheduling problems, which will greatly increase the workload of the scheduling. In this paper, the disturbances are classified as strong disturbance and weak disturbance, and a rescheduling driven rule of strong disturbance is designed. Especially, the various disturbances impact on the production plan could be reflected in a time change, so a rescheduling model under time strong disturbance is established. In this paper, a hyper-heuristic genetic algorithm (HHGA) is designed based on the heuristic rules and genetic algorithms. This combination is intended to reduce the generation time of rescheduling plan under the strong disturbance. In the end, the simulation results indicate the success with the HHGA.

Partial/Parallel Disassembly Sequence Planning for Complex Products

Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning (DSP) is a challenging combinatorial optimization problem due to complex constraints of many products. This paper considers partial and parallel disassembly sequence planning for solving the degrees-of-freedom in modular product design, considering disassembly time, cost, and energy consumption. An automatic self-decomposed disassembly precedence matrix (DPM) is designed to generate partial/parallel disassembly sequence for reducing complexity and improving efficiency. A Tabu search-based hyper heuristic algorithm with exponentially decreasing diversity management strategy is proposed. Compared with the low-level heuristics, the proposed algorithm is more efficient in terms of exploration ability and improving energy benefits (EBs). The comparison results of three different disassembly strategies prove that the partial/parallel disassembly has a great advantage in reducing disassembly time, and improving EBs and disassembly profit (DP).

A PSO Inspired Asynchronous Cooperative Distributed Hyper-Heuristic for Course Timetabling Problems

This paper presents a novel approach for asynchronous cooperative hyper-heuristic incorporated with particle swarm optimisation which inspired by social individual behaviour of swarm intelligence, like bird flocking and fish schooling. The proposed hyper-heuristic algorithm starts with a complete solution and tries to improve the soft constraints, whilst always remaining in the feasible region of the search space. The performances of the proposed cooperative hyper-heuristics are evaluated using the standard course timetabling benchmark problem. From the experimental results, it shows that the proposed Asynchronous Cooperative Distribute Low-level heuristics (ACDLLHs) algorithm is able to find new best solutions for all five medium problem instances and shared optimal solutions for all five small instances. When coupled with two, four and six agents, the Asynchronous Cooperative Distributed Hyper-heuristic (ACDHH) algorithm is able to improve the solution quality for a large instance.

Evolutionary and population-based methods versus constructive search strategies in dynamic combinatorial optimization

Optimization in dynamic environments is a hot research area that has attracted a notable attention in the past decade. It is clear from the dynamic optimization literature that most of the effort is devoted to continuous dynamic optimization problems although majority of the real-life problems are combinatorial. Additionally, in comparison to evolutionary or population-based approaches, constructive search strategy, which is shown to be successful in stationary combinatorial optimization problems, is commonly ignored by the dynamic optimization community. In the present work, a constructive and multi-start search strategy is proposed to solve dynamic multi-dimensional knapsack problem, which has numerous applications in real world. Making use of constructive and multi-start features, the aim here is to test the performance of such a strategy and to observe its behavior in dynamically changing environments. In this regard, this strategy is compared to the well-known evolutionary and population-based approaches, including a Genetic Algorithm-based memetic algorithm, Differential Evolution algorithm, Firefly Algorithm and a hyper-heuristic, which employs these population-based algorithms as low-level heuristics in accordance with their individual contributions. Furthermore, in order to improve their performances in dynamic environments, the mentioned evolutionary algorithms are enhanced by using triggered random immigrants and adaptive hill climbing strategies. As one can see from the comprehensive experimental analysis, while the proposed approach outperforms most of the evolutionary-based approaches, it is outperformed by firefly and hyper-heuristic algorithms in some of the instances. This points out competiveness of the proposed approaches. Finally, according to the statistical results of non-parametric tests, one can conclude that the proposed approach can be considered as a promising and a competitive algorithm in dynamic environments.

A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem

Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as an important class of problems in modern supply chains and manufacturing systems. In this paper, a backtracking search hyper-heuristic (BS-HH) algorithm is proposed to solve the DAPFSP. In the BS-HH scheme, ten simple and effective heuristic rules are designed to construct a set of low-level heuristics (LLHs), and the backtracking search algorithm is employed as the high-level strategy to manipulate the LLHs to operate on the solution space. Additionally, an efficient solution encoding and decoding scheme is proposed to generate a feasible schedule. The effectiveness of the BS-HH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.

An Improved Hyper-Heuristic Clustering Algorithm for Wireless Sensor Networks

Clustering is one of the most famous open problems of wireless sensor network (WSN) that has been studied for years because all the sensors in a WSN have only a limited amount of energy. As such, the so-called low-energy adaptive clustering hierarchy (LEACH) was presented to prolong the lifetime of a WSN. Although the original idea of LEACH is to keep each sensor in a WSN from being chosen as a cluster head (CH) too frequently so that the loading of the sensors will be balanced, thus avoiding particular sensors from running out of their energy quickly and particular regions from failing to work, it is far from perfect because LEACH may select an unsuitable set of sensors as the cluster heads. In this paper, a high-performance hyper-heuristic algorithm will be presented to enhance the clustering results of WSN called hyper-heuristic clustering algorithm (HHCA). The proposed algorithm is designed to reduce the energy consumption of a WSN, by using a high-performance metaheuristic algorithm to find a better solution to balance the residual energy of all the sensors so that the number of alive sensor nodes will be maximized. To evaluate the performance of the proposed algorithm, it is compared with LEACH, LEACH with genetic algorithm, and hyper-heuristic algorithm alone in this study. Experimental results show that HHCA is able to provide a better result than all the other clustering algorithms compared in this paper, in terms of the energy consumed.

A Hybrid Approach of Genetic Algorithm and Multi Objective PSO Task Scheduling in Cloud Computing

The genetic algorithm is an evolutionary optimization algorithm based upon Initial population, crossover, mutation and Evaluation. On the other side, Multi Objective particle swarm optimization (MOPSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability. In high-performance hyper-heuristic algorithm is used to find better scheduling solutions in cloud computing. To improve the scheduling results in terms of makespan, throughput, cost. Hyper-heuristic algorithm finds better scheduling solutions for cloud computing systems and to further improve the scheduling results in terms of make span. A novel Multi objective particle swarm optimization and Genetic Algorithm based hyper-heuristic resource scheduling algorithm has been designed as the hybrid algorithm. Performance of the proposed algorithm has also been evaluated through the Cloud Sim toolkit. We have compared our hybrid scheduling algorithm with existing common heuristic-based scheduling algorithms. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving reduce cost and improve makespan. The proposed model shows the improved resource utilization, makespan, throughput.