Hyper-heuristic Evolutionary Algorithm Research Articles

Collaborative Q-learning hyper-heuristic evolutionary algorithm for the production and transportation integrated scheduling of silicon electrodes

Silicon electrodes are widely used in semiconductor etching machines. The periodic consumption of silicon electrodes has become an important consumable in wafer manufacturing. Due to the limited number of silicon electrode manufacturers and the increasing demand for silicon electrodes in global market, the newly processed silicon electrodes need to be immediately delivered to wafer manufacturers to ensure uninterrupted production. Driven by this, this paper introduces a new integrated scheduling problem called the silicon electrode production and transportation integrated scheduling problem (SEPTISP), whose criterion is to minimize the makespan. This problem is modeled as a combination of the production scheduling subproblem (PSS) and the transportation scheduling subproblem (TSS). These two subproblems are coupled with each other. Considering the SEPTISP's NP-hard and coupling properties, a novel collaborative Q-learning hyper-heuristic evolutionary algorithm integrated with reinforcement learning scheme (CQHEA_RLS) is proposed to handle them. In the CQHEA_RLS, the production agent and the transportation agent are focused on executing exploitation in the reduced but promising regions via their specific search actions, while the joint agent is centered on performing exploration in the whole solution space through its joint search actions and determining the suitable search regions for the other two agents. Meanwhile, the joint agent also integrates the historical performance information of its own and the other two agents' search actions to dynamically choose its subsequent search actions. Moreover, each agent is devised as an effective Q-learning hyper-heuristic evolutionary algorithm. This new collaborative framework ensures that the CQHEA_RLS can continuously search downwards to obtain truly high-quality solutions in a relatively short runtime. The experimental results demonstrate that the designed algorithm can achieve better performance than the existing state-of-the-art algorithms.

A Q-learning-based hyper-heuristic evolutionary algorithm for the distributed flexible job-shop scheduling problem with crane transportation

A Q-learning-based hyper-heuristic evolutionary algorithm for the distributed flexible job-shop scheduling problem with crane transportation

Q-learning-based hyper-heuristic evolutionary algorithm for the distributed assembly blocking flowshop scheduling problem

Distributed shop scheduling problems (DSSPs) have attracted increasing interest in recent years due to the technical trends of smart manufacturing and Industry 4.0. The distributed assembly blocking flowshop scheduling problem (DABFSP) is a critical class of DSSPs with widespread applications in modern supply chains and manufacturing systems. In this paper, a Q-learning-based hyper-heuristic evolutionary algorithm (QLHHEA) is proposed to solve DABFSP with the objective of minimizing the makespan. Firstly, a mathematical model of DABFSP is formulated, and two insertion-based speedup strategies are devised to conserve the computational cost of evaluating solutions and to accelerate the search efficiency. Secondly, a problem-specific constructive heuristic is developed to produce high-quality initial solutions. Thirdly, twelve efficient heuristics are designed to construct low-level heuristics (LLHs). The Q-learning-based evolutionary algorithm is applied as a high-level strategy to manipulate the LLHs, which are then executed in order to search the solution space. Moreover, suitable solution encoding and decoding schemes are provided to produce feasible scheduling schedules. The design of experiments is implemented to investigate the impact of the parameters. Finally, a comprehensive comparison campaign is carried out based on a total of 1710 well-known instances to evaluate the efficacy of the proposed algorithm against several state-of-the-art algorithms. Experimental results and statistical analysis show that QLHHEA significantly outperforms the existing algorithms by a significant margin, demonstrating the effectiveness and efficiency of QLHHEA in solving DABFSP.

Q-Learning-Based Hyperheuristic Evolutionary Algorithm for Dynamic Task Allocation of Crowdsensing.

Task allocation is a crucial issue of mobile crowdsensing. The existing crowdsensing systems normally select the optimal participants giving no consideration to the sudden departure of mobile users, which significantly affects the sensing quality of tasks with a long sensing period. Furthermore, the ability of a mobile user to collect high-precision data is commonly treated as the same for different types of tasks, causing the unqualified data for some tasks provided by a competitive user. To address the issue, a dynamic task allocation model of crowdsensing is constructed by considering mobile user availability and tasks changing over time. Moreover, a novel indicator for comprehensively evaluating the sensing ability of mobile users collecting high-quality data for different types of tasks at the target area is proposed. A new Q -learning-based hyperheuristic evolutionary algorithm is suggested to deal with the problem in a self-learning way. Specifically, a memory-based initialization strategy is developed to seed a promising population by reusing participants who are capable of completing a particular task with high quality in the historical optima. In addition, taking both sensing ability and cost of a mobile user into account, a novel comprehensive strength-based neighborhood search is introduced as a low-level heuristic (LLH) to select a substitute for a costly participant. Finally, based on a new definition of the state, a Q -learning-based high-level strategy is designed to find a suitable LLH for each state. Empirical results of 30 static and 20 dynamic experiments expose that this hyperheuristic achieves superior performance compared to other state-of-the-art algorithms.

A Hybrid Hyper-Heuristic Flower Pollination Algorithm for Service Composition Problem in IoT

Objectives: The modern science applications have non-continuous and multivariate nature due to which the traditional optimization methods suffer a lack of efficiency. Flower pollination is a natural interesting procedure in the real world. The novel optimization algorithms can be designed by employing the evolutionary capability of the flower pollination to optimize resources. Methods: This paper introduces the hybrid algorithm named Hybrid Hyper-Heuristic Flower Pollination Algorithm, HHFPA. It uses a combination of Flower Pollination Algorithm (FPA) and Hyper- Heuristic Evolutionary Algorithm (HypEA). This paper compares the basic FPA with the proposed algorithm named HHFPA. FPA is inspired by the pollination process of flowers whereas the hyper-heuristic evolutionary algorithm operates on the heuristics search space that contains all the heuristics to find a solution for a given problem. The proposed algorithm is implemented to solve the Quality of Service (QoS) based Service Composition Problem (SCoP) in Internet of Things (IoT). With increasing services with same functionality on the web, selecting a suitable candidate service based on non-functional characteristics such as QoS has become an inspiration for optimization. Results: This paper includes experimental results showing better outcomes to find the best solution using the proposed algorithm as compared to Basic FPA. Conclusion: The empirical analysis also reveals that HHFPA outperformed basic FPA in solving the SCoP with more convergence rates.

Optimization of multi-receiver SNRs for indoor visible light communication based on modified evolutionary algorithm

Based on indoor visible light communication (VLC) technology, the large-scale LED array can be used for the integration system of communication, positioning and illumination. However, due to the different propagation time between LEDs and receivers, the signal to noise ratio (SNR) is affected by the inter symbol interference (ISI). This paper proposes a multi-receiver SNR optimization method by selecting communicating LEDs in the LED array. The proposed method uses the modified evolutionary algorithm (EA) to obtain the optimal SNR. In the processes of initialization, crossover and mutation operations, instead of general stochastic generation and transformation, the heuristic gene initialization, maximum contributing gene reservation and the minimum contributing gene elimination, which are specially designed for VLC LED selection problem, are proposed to improve the EA. The delay features of different LEDs to receivers are quantitatively analyzed and used as the criteria to gene initialization, crossover and mutation in order to attenuate the ISI. Simulation results reveal that the proposed method converges faster and obtains larger SNRs than the general and the hyper heuristic EA.

Optimization-aided calibration of an urban microclimate model under uncertainty

Simulation models play an important role in the design, analysis, and optimization of modern energy and environmental systems at building or urban scale. However, due to the extreme complexity of built environments and the sheer number of interacting parameters, it is difficult to obtain an accurate representation of real-world systems. Thus, model calibration and uncertainty analysis hold a particular interest, and it is necessary to evaluate to what degree simulation models are imperfect before implementing them during the decision-making process. In contrast to the extensive literature on the calibration of building performance models, little has been reported on how to automatically calibrate physics-based urban microclimate models. This paper illustrates a general methodology for automatic model calibration and applies it to an urban microclimate system. The Urban Weather Generator (UWG) is selected as the underlying simulation engine for an optimization-aided calibration based on the urban outdoor air temperature in an existing district area located in downtown Abu Dhabi (UAE) during 2017. In particular, given the time-constrained nature of engineering applications, an online hyper-heuristic evolutionary algorithm (EA) is proposed and developed in order to accelerate the calibration process. The validation results show that, in single-objective optimization, the online hyper-heuristics could robustly help EA produce quality solutions with smaller uncertainties at much less computational cost. In addition, the resulting calibrated solutions are able to capture weekly-average and hourly diurnal profiles of the urban outdoor air temperature similar to the measurements for certain periods of the year.

A customized classification algorithm for credit card fraud detection

This paper presents Fraud-BNC, a customized Bayesian Network Classifier (BNC) algorithm for a real credit card fraud detection problem. The task of creating Fraud-BNC was automatically performed by a Hyper-Heuristic Evolutionary Algorithm (HHEA), which organizes the knowledge about the BNC algorithms into a taxonomy and searches for the best combination of these components for a given dataset. Fraud-BNC was automatically generated using a dataset from PagSeguro, the most popular Brazilian online payment service, and tested together with two strategies for dealing with cost-sensitive classification. Results obtained were compared to seven other algorithms, and analyzed considering the data classification problem and the economic efficiency of the method. Fraud-BNC presented itself as the best algorithm to provide a good trade-off between both perspectives, improving the current company’s economic efficiency in up to 72.64%.

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.

A balanced fuzzy Cultural Algorithm with a modified Levy flight search for real parameter optimization

Over the last few decades, a plethora of improved evolutionary algorithms was developed with exquisite performance on numerical and real-world problems. Among such algorithms, the Cultural Algorithm is a hyper-heuristic evolutionary algorithm, which explicitly utilizes the knowledge represented in the belief space as an essential component to guide the evolutionary search. In this paper, a new enhanced Cultural Algorithm incorporates a fuzzy system with a modified Levy flight search that is introduced as a new component. The new algorithm namely, b-fCA+mLF, utilizes a balanced search mode using a customized belief space with a quality function to harmonize how the knowledge sources work in parallel. The communication protocols between the population space and the belief space are established through the modified fuzzy acceptance and influence functions. Using these new functions, the best individuals are selected to create new knowledge in an effective manner. Similarly, the best knowledge is selected to evolve the individuals in the population space and guide the evolutionary search towards the promising regions. A modified Levy flight search is proposed and utilizes the information from the belief space as an input to support the evolution process to generate better solutions. The algorithm is tested on the benchmark suite taken from the IEEE-CEC’15 competition on learning-based real-parameter single objective optimization, and is compared with other algorithms including the best performer algorithms in this competition. The results suggest that the proposed algorithm is statistically better and is able to produce higher quality solutions than the other state-of-the-art algorithms. A case study on the well-known 120-bar dome truss design problem is also presented to test the validity of the proposed algorithm for the solution of complex design problems. The results of this problem show the ability of the proposed algorithm to generate good solutions with fewer function evaluations, compared to reported results in the literature and other well-known algorithms.

Hyperheuristic‐based analysis and optimization of a mobile indoor visible light communication system

AbstractIn this paper, we study a mobile indoor visible light communication system that can be used for the creation of the indoor network infrastructure for sending information besides serving its main purpose of illumination. It uses visible light communication technology where the light‐emitting diodes (LEDs) are deployed on the ceiling of the room and the receiver is kept mobile at a certain height from the ground level. This paper focuses on the optimal deployment of the LEDs with an objective to achieve the requisite level of communication performance without any transmission error while maintaining ubiquitous receiver mobility. The hyperheuristic evolutionary algorithm (HypEA) has been implemented to analyze and optimize the energy consumption through optimal placement of LEDs under variation of semiangle values. Furthermore, the implemented optimization algorithm investigates the other system related parameters including average outage area ratio, signal‐to‐noise ratio, and root mean square delay for a given threshold average bit error ratio. Finally, The obtained results are illustrated through numerical plots, which reveal that the HypEA is efficient in optimizing the system to minimize the overall power consumption under maximum receiver mobility.

Hyper-heuristic approach for multi-objective software module clustering

In the software maintenance phase of software development life cycle, one of the main concerns of software engineers is to group the modules into clusters with maximum cohesion and minimum coupling.To analyze the efficacy of Multi-objective Hyper-heuristic Evolutionary Algorithm (MHypEA) in solving real-world clustering problems and to compare the results with the reported results in the literature for single as well as multi-objective formulations of the problem and also to present a CASE tool that assists software engineers in software module clustering process.The paper reports on empirical evaluation of the performance of MHypEA with the reported results in the literature. The comparison is mainly based on two factors – quality of the obtained solutions and the computational effort.On all the attempted problems, MHypEA reported good results in comparison to all the studies that were reported on multi-objective formulation of the problem, with a computational effort of nearly one-twentieth of the computational effort required by the other multi-objective algorithms.The hyper-heuristic approach is able to produce high quality clustered systems with less computational effort.

Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.

Investigating fitness functions for a hyper-heuristic evolutionary algorithm in the context of balanced and imbalanced data classification

In this paper, we analyse in detail the impact of different strategies to be used as fitness function during the evolutionary cycle of a hyper-heuristic evolutionary algorithm that automatically designs decision-tree induction algorithms (HEAD-DT). We divide the experimental scheme into two distinct scenarios: (1) evolving a decision-tree induction algorithm from multiple balanced data sets; and (2) evolving a decision-tree induction algorithm from multiple imbalanced data sets. In each of these scenarios, we analyse the difference in performance of well-known classification performance measures such as accuracy, F-Measure, AUC, recall, and also a lesser-known criterion, namely the relative accuracy improvement. In addition, we analyse different schemes of aggregation, such as simple average, median, and harmonic mean. Finally, we verify whether the best-performing fitness functions are capable of providing HEAD-DT with algorithms more effective than traditional decision-tree induction algorithms like C4.5, CART, and REPTree. Experimental results indicate that HEAD-DT is a good option for generating algorithms tailored to (im)balanced data, since it outperforms state-of-the-art decision-tree induction algorithms with statistical significance.

Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.

Scheduling and Inspection Planning in Software Development Projects Using Multi-Objective Hyper-Heuristic Evolutionary Algorithm

Scheduling and Inspection Planning in Software Development Projects Using Multi-Objective Hyper-Heuristic Evolutionary Algorithm