Set Of Heuristics Research Articles

On branching heuristics for the bi-objective 0/1 unidimensional knapsack problem

This paper focuses on branching strategies that are involved in branch and bound algorithms when solving multi-objective optimization problems. The choice of the branching variable at each node of the search tree constitutes indeed an important component of these algorithms. In this work we focus on multi-objective knapsack problems. In the literature, branching heuristics used for these problems are static, i.e., the order on the variables is determined prior to the execution. This study investigates the benefit of defining more sophisticated branching strategies. We first analyze and compare a representative set of classic branching heuristics and conclude that none can be identified as the best overall heuristic. Using an oracle, we highlight that combining branching heuristics within the same branch and bound algorithm leads to considerably reduced search trees but induces high computational costs. Based on learning adaptive techniques, we propose then dynamic adaptive branching strategies that are able to select the suitable heuristic to apply at each node of the search tree. Experiments are conducted on the bi-objective 0/1 unidimensional knapsack problem.

The room they want to hear: Subjective preference of reverberation in vocal recordings

Audio professionals in the digital era have hundreds of reverberation options at their disposal, from the uber-realistic to the dynamic and creative. Often the choice of reverb is made with a set of heuristics—based on the engineer’s experience and opinion for what achieves the best musical and dramatic effect. In this presentation, we turn to the listener and explore their preferences of reverberation on vocal performances. Through a series of experiments, we evaluate the aesthetic preferences of the casual music consumer and compare them to those of the experienced audio engineer. In addition, we examine factors that may predispose listeners to prefer certain reverberation characteristics over others.

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.

Design and Implementation of Task Reprocessing on Medium-large Multi-core Architecture

EDF-Based real-time scheduling approach is one of the efficient way of scheduling the recurrent real-time tasks in both soft real-time and hard real-time systems. EDF algorithms entile heavy overhead due to lower scheduling and huge migration. In this paper, an exploratory procedure has been presented for reprocessing of neglected task sets and also examine, whether an improved heuristic set attributed as Enhanced shared CAche Performance (ENCAP) can improve the shared cache performance which processes the task set based on Earliest-Deadline-First (EDF). An Object-Oriented real-time code reusable scheduling components which is designed from the scratch referred as LITMUSRT has been used to test the efficiency and average-case accomplishments where per task utilizations are shortened and overall utilization are not restricted. We also discuss the implementation and experimental evaluation of Task-Repo procedure for co-scheduling task sets, under the utilization of 50-60% (per-task) on small to medium-large multicore LINUX SYSTEM.

Energy-Oriented Modeling and Optimization of a Heat Treating Furnace

In this paper, we develop an energy-focused model of an industrial roller hearth heat treating furnace. The model represents radiation heat transfer with nonparticipating gas and convective heat transfer. The model computes the exit temperature profile of the treated steel parts and the energy consumption and efficiency of the furnace. We propose a dual iterative numerical scheme to solve the conservation equations and validate its efficacy by simulating the dynamics of the furnace during startup, as well as for steady-state operation. A case study investigates energy consumption within the furnace under temperature control. We first consider a heuristic control strategy using simple linear controllers. A response surface approach is then used to find the optimal set points that minimize energy consumption while ensuring desired part temperature properties are met when processing is complete. With optimized set points, 4.8% less energy per part is required versus the heuristic set points.

GameFlow in Different Game Genres and Platforms

The GameFlow model strives to be a general model of player enjoyment, applicable to all game genres and platforms. Derived from a general set of heuristics for creating enjoyable player experiences, the GameFlow model has been widely used in evaluating many types of games, as well as non-game applications. Initial applications of the GameFlow model were limited to real-time strategy games. However, in order to be considered a general model of player enjoyment in games, the GameFlow model needs to be applied to a more varied set of play experiences. In this article, we revisit the design of the GameFlow model, review the various applications and derivative models, and discuss on-going analysis of the model. Subsequently, we describe a study that aims to extend the initial validation of the GameFlow model to incorporate additional game types. We report the results of expert reviews conducted using the GameFlow criteria to evaluate first person shooter games on Sony PlayStation 3 and adventure games on Apple iPhone. Our findings provide insight into the manifestation of the GameFlow elements in these types of games and also highlight some of the genre-specific considerations in the application of the GameFlow model. Key issues are raised and discussed in relation to immersion, social interaction, and multiplayer games.

Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems

In this article, a multi-objective evolutionary framework to build selection hyper-heuristics for solving instances of the 2D bin packing problem is presented. The approach consists of a multi-objective evolutionary learning process, using specific tailored genetic operators, to produce sets of variable length rules representing hyper-heuristics. Each hyper-heuristic builds a solution to a given problem instance by sensing the state of the instance, and deciding which single heuristic to apply at each decision point. The hyper-heuristics consider the minimization of two conflicting objectives when building a solution: the number of bins used to accommodate the pieces and the total time required to do the job. The proposed framework integrates three well-studied multi-objective evolutionary algorithms to produce sets of Pareto-approximated hyper-heuristics: the Non-dominated Sorting Genetic Algorithm-II, the Strength Pareto Evolutionary Algorithm 2, and the Generalized Differential Evolution Algorithm 3. We conduct an extensive experimental analysis using a large set of 2D bin packing problem instances containing convex and non-convex irregular pieces, under many conditions, settings and using several performance metrics. The analysis assesses the robustness and flexibility of the proposed approach, providing encouraging results when compared against a set of well-known baseline single heuristics.

Optimization of multiple PON deployment costs and comparison between GPON, XGPON, NGPON2 and UDWDM PON

In this paper we propose an optimization framework for multiple deployment of PON in a wide region with very large number of users, with different bit rate demands, serviced by many central offices, as it may practically happen in a large city that plans a massive introduction of Fiber to the Home technologies using PON. We propose an algorithm called Optimal Topology Search (OTS), which is based on a set of heuristic approaches, capable of performing an optimal dimensioning of multiple PON deployments for a set of central offices (CO), including an optimal distribution of users among the CO. The set of heuristics integrated in OTS permit the efficient clustering of users for each CO, depending on their location and the bit rate demanded by them. It also permits the definition of optimal routes for optical cables and the allocation of branching devices. Taking into account hardware capacity restrictions and physical layer restrictions, we obtained solutions for different types of standardized PON technologies, like GPON, XGPON and NGPON2 as well as for future UDWDM-PON. We evaluate the optimal network deployment in a series of different minimum guaranteed bit rate demand scenarios, employing realistic maps of a large city in order to compare costs and portrait some reference points for deciding in which scenario a specific technology constitutes the best choice.

How to develop usability heuristics: A systematic literature review

Numerous sets of usability heuristics have been designed for specific domains, as traditional heuristics do not evaluate the specific features of particular applications. A substantial portion of the existing research focuses on designing new sets of heuristics, demonstrating the interest in heuristics that might identify specific usability problems for specific application domains. This article presents an exhaustive review of 73 studies related to usability heuristics for specific domains and methodologies; the objective is to identify the approach that is used to create usability heuristics and whether a formal and systematic process is involved. We conducted a systematic literature review of papers published between 2006 and 2016. The review was based on the guidelines proposed by Kitchenham (2007) [13] and shows that most studies use an informal process to develop usability heuristics. The creation of heuristics is mainly based on existing heuristics, literature reviews, usability problems, and guidelines. However, a few studies apply a methodology to define, validate and refine the set of heuristics proposed. Nevertheless, these methodologies should be formalized in order to help the process of creating usability heuristics.

A Hyperheuristic Methodology to Generate Adaptive Strategies for Games

Hyperheuristics have been successfully applied in solving a variety of computational search problems. In this paper, we investigate a hyperheuristic methodology to generate adaptive strategies for games. Based on a set of low-level heuristics (or strategies), a hyperheuristic game player can generate strategies which adapt to both the behavior of the co-players and the game dynamics. By using a simple heuristic selection mechanism, a number of existing heuristics for specialized games can be integrated into an automated game player. As examples, we develop hyperheuristic game players for three games: iterated prisoner's dilemma, repeated Goofspiel and the competitive traveling salesmen problem. The results demonstrate that a hyperheuristic game player outperforms the low-level heuristics, when used individually in game playing and it can generate adaptive strategies even if the low-level heuristics are deterministic. This methodology provides an efficient way to develop new strategies for games based on existing strategies.

Spam Mobile Apps

The increased popularity of smartphones has attracted a large number of developers to offer various applications for the different smartphone platforms via the respective app markets. One consequence of this popularity is that the app markets are also becoming populated with spam apps. These spam apps reduce the users’ quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such as apps not having a specific functionality, those having unrelated app descriptions or unrelated keywords, or similar apps being made available several times and across diverse categories. Market operators maintain antispam policies and apps are removed through continuous monitoring. Through a systematic crawl of a popular app market and by identifying apps that were removed over a period of time, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. We build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy of over 95% with precision varying between 85% and 95% and recall varying between 38% and 98%. We further show that a limited number of features, in the range of 10--30, generated from app metadata is sufficient to achieve a satisfactory level of performance. On a set of 180,627 apps that were present at the app market during our crawl, our classifier predicts 2.7% of the apps as potential spam. Finally, we perform additional manual verification and show that human reviewers agree with 82% of our classifier predictions.

DataMock: An Agile Approach for Building Data Models from User Interface Mockups

In modern software development, much time is devoted and much attention is paid to the activity of data modeling and the translation of data models into databases. This has motivated the proposal of different approaches and tools to support this activity, such as semiautomatic approaches that generate data models from requirements artifacts using text analysis and sets of heuristics, among other techniques. However, these approaches still suffer from important limitations, including the lack of support for requirements traceability, the poor support for detecting and solving conflicts in domain-specific requirements, and the considerable effort required for manually checking the generated models. This paper introduces DataMock, an Agile approach that enables the iterative building of data models from requirements specifications, while supporting traceability and allowing inconsistencies detection in data requirements and specifications. The paper also describes how the approach effectively allows improving traceability and reducing errors and effort to build data models in comparison with traditional, state-of-the-art, data modeling approaches.

A methodology for determining an effective subset of heuristics in selection hyper-heuristics

We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic using multi-armed bandits coupled with a change detection mechanism. The methodology is tested on two real-world optimization problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances.

A comprehensible guide to a new unifier for CIC including universe polymorphism and overloading

AbstractUnification is a core component of every proof assistant or programming language featuring dependent types. In many cases, it must deal with higher order problems up to conversion. Since unification in such conditions is undecidable, unification algorithms may include several heuristics to solve common problems. However, when the stack of heuristics grows large, the result and complexity of the algorithm can become unpredictable. Our contributions are twofold: (1) We present a full description of a new unification algorithm for the Calculus of Inductive Constructions (the base logic of COQ), building it up from a basic calculus to the full Calculus of Inductive Constructions as it is implemented in COQ, including universe polymorphism, canonical structures (the overloading mechanism baked into COQ's unification), and a small set of useful heuristics. (2) We implemented our algorithm, and tested it on several libraries, providing evidence that the selected set of heuristics suffices for large developments.

A multilevel hyper-heuristic for solving Max-SAT

A hyper-heuristic is a high-level method that manages a set of low-level heuristics to solve various problems in a problem-independent manner. In this paper, we propose a new selection hyper-heuristic with the multilevel paradigm. The multilevel paradigm refers to the process of dividing large problems into sub-problems. Each sub-problem is being solved to reach an optimal solution by using the resulting solution from a previous level as a starting solution at the next level. The selection strategy chooses the adequate low-level heuristic at any iteration during the search. For analysis purposes, several variants of hyper-heuristics are implemented and Max-SAT is used as the test bed. The experimental results revealed that the multilevel paradigm together with a new hybrid-heuristic selection mechanism provides a substantial performance improvement. A comparison with two known state of the art algorithms that are GSAT and WALKSAT is given to further show the efficiency of our method.

Resource-Constrained Task Assignment for Event-Driven Sensing Using Public Bicycles in Smart Cities

Many cities have provided public bicycle services to reduce traffic congestion and air pollution. Mobility of public bicycles makes them very suitable for event-driven sensing for smart cities, i.e., collecting data relevant to special events such as car accidents or street parades. The problem is how to assign a set of bicycles to best fulfil the sensing mission, considering the constrained storage, battery energy, and communication capability of bicycles. The problem is referred to as the resource-constrained task assignment for event-driven sensing (ReConTAES). The goal is to minimise the number of bicycles used, while balancing the energy consumption of the selected bicycles. We first formulate the problem as a mixed integer programming and then propose a set of greedy heuristics to solve the problem. We evaluate the proposed algorithms by using real trajectories to show their feasibility.

A multilevel hyper-heuristic for solving Max-SAT

A hyper-heuristic is a high-level method that manages a set of low-level heuristics to solve various problems in a problem-independent manner. In this paper, we propose a new selection hyper-heuristic with the multilevel paradigm. The multilevel paradigm refers to the process of dividing large problems into sub-problems. Each sub-problem is being solved to reach an optimal solution by using the resulting solution from a previous level as a starting solution at the next level. The selection strategy chooses the adequate low-level heuristic at any iteration during the search. For analysis purposes, several variants of hyper-heuristics are implemented and Max-SAT is used as the test bed. The experimental results revealed that the multilevel paradigm together with a new hybrid-heuristic selection mechanism provides a substantial performance improvement. A comparison with two known state of the art algorithms that are GSAT and WALKSAT is given to further show the efficiency of our method.

Minimizing Carbon Dioxide Emissions Due to Container Handling at Marine Container Terminals via Hybrid Evolutionary Algorithms

Considering a rapidly increasing seaborne trade and drastic climate changes due to emissions, produced by oceangoing vessels and container handling equipment, marine container terminal operators not only have to improve effectiveness of their operations to serve the increasing demand, but also to account for the environmental impact associated with the terminal operations. This paper proposes a novel mixed integer mathematical model for the berth scheduling problem, which minimizes the total service cost of vessels, including the total carbon dioxide emission cost due to container handling. The latter pollutant is a primary greenhouse gas that causes global warming. A Hybrid Evolutionary Algorithm, which deploys a set of local search heuristics, is developed to solve the problem. Computational experiments showcase that the optimality gap of the proposed solution algorithm does not exceed 1.61%. It is further shown that the application of additional local search heuristics allows efficient discovery of promising solutions throughout the search process. Results from numerical experiments also indicate that changes in the carbon dioxide emission cost may significantly affect the design of berth schedules. The developed mathematical model and the proposed solution algorithm can thus be adopted as effective planning tools by the marine container terminal operators and improve the environmental sustainability of the terminal operations.

Growing Some Backbone? Network Governance and the Washtenabe Regional Homelessness Assistance Network

Public service leaders in communities across the United States (and even across other parts of the world) have a growing interest in the development and implementa-tion of collaborative community networks, usually including large numbers of organi-zations and agencies from each of the three sectors (business, nonprofit, and govern-ment), for the purpose of tackling specific, complex community issues or problems that they have found are not possible for any one, two, or even a few organizations to address or resolve successfully. The kinds of problems these networks emerge to ad-dress tend to be perceived as “wicked problems”—problems that are very resistant to resolution because they are complex and multifaceted, involving multiple institutions, organizations, actors, and decision makers, typically from different sectors and profes-sions, each bringing to the problem a set of heuristics and perspectives that reflect a myriad of competing and conflicting values, interests, knowledge bases, and expertise (Churchman, 1967; Weber & Khademian, 2008)...Purchase this special issue to continue reading

Allocation of Resource Dynamically in Cloud Computing Environment Using Virtual Machines

Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this research paper, we present a system that uses virtualization technology to allocate data centre resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.