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In this paper, we present a novel hybrid metaheuristic for the Knapsack Problem with Forfeits (KPF). KPF is a recently introduced generalization of the Knapsack Problem. In this variant, a penalty cost incurs whenever both items composing a so-called forfeit pair belong to the solution. Our proposed algorithm, called GA–CG Forfeits, combines the strengths of the Genetic and Carousel Greedy paradigms. In this work, we define the algorithm and compare it with two previously proposed heuristics on a set of benchmark instances. In these tests, GA–CG Forfeits provided significantly better solutions than the other two algorithms on all instances. [ABSTRACT FROM AUTHOR]

A large amount of data available on Web has proven to be an immense resource for innovative recommender system (RS) techniques and concepts. The traditional recommender system intended to provide recommendations for a single user. However, in certain domains the recommendation is required for a group of users. As to provide better recommendations for a group of users, we leverage the concept of circles in a network. In this work, we use the genetic algorithm (GA) K_Means clustering algorithm to generate social circles in a network. Then, we compute the status of each user in these overlapping circles. Finally, a circle-based group recommendation approach is used to generate the final group recommendation. The results obtained on the Epinions dataset validate the eminence of the proposed model over traditional approaches of group recommendation. [ABSTRACT FROM AUTHOR]

In construction industry, general contractors (GCs) need to manage and conduct numerous projects simultaneously. To this aim, they usually have subcontractors conduct available tasks and projects. So, subcontractor management is becoming a major challenge. To deal with this challenge, GCs aim to reduce total costs of projects including employment/subcontracting costs, indirect costs, tardiness penalties, and the money which must be paid for movement of workforce from one project to another. The aim of current research study is to propose a model for selecting subcontractors and assigning available tasks in the project to them in order to reduce the costs of the GC. Then, a genetic algorithm is proposed to solve a real problem. The proposed algorithm is innovative from three points of view: (1) generation of initial population; (2) subcontractor assignment approach; and (3) the objective function. The problem was also solved by means of an exact method. Then, the results of proposed algorithm were compared to the outcomes of the exact method. This comparison shows that the proposed algorithm can efficiently help GCs select subcontractors and assign available tasks to each subcontractor when several projects must be carried out simultaneously. [ABSTRACT FROM AUTHOR]

Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the most important ones. Generally, computational strategies have high performance and yield poor results in terms of efficiency, in contrast, meta-heuristic strategies have good efficiency and lower performance. Among the strategies available, some are efficient strategies but suffer from low performance; conversely, some others have good performance, but is not such efficient. In general, there is not a strategy that enjoys both above-mentioned metrics. In this paper, it is tried to combine the genetic algorithm and the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity to produce the appropriate test suite in terms of efficiency and performance. Also, a simple and effective minimizing function is employed to increase efficiency. The evaluation results show that the proposed strategy outperforms the existing approaches in terms of both efficiency and performance. [ABSTRACT FROM AUTHOR]

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master. [ABSTRACT FROM AUTHOR]

Forming a team of experts that can match the requirements of a collaborative task is an important aspect, especially in project development. In this paper, we propose an improved Jaya optimization algorithm for minimizing the communication cost among team experts to solve team formation problem. The proposed algorithm is called an improved Jaya algorithm with a modified swap operator (IJMSO). We invoke a single-point crossover in the Jaya algorithm to accelerate the search, and we apply a new swap operator within Jaya algorithm to verify the consistency of the capabilities and the required skills to carry out the task. We investigate the IJMSO algorithm by implementing it on two real-life datasets (i.e., digital bibliographic library project and StackExchange) to evaluate the accuracy and efficiency of proposed algorithm against other meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, African buffalo optimization algorithm and standard Jaya algorithm. Experimental results suggest that the proposed algorithm achieves significant improvement in finding effective teams with minimum communication costs among team members for achieving the goal. [ABSTRACT FROM AUTHOR]

In the ensemble feature selection method, if the weight adjustment is performed on each feature subset used, the ensemble effect can be significantly different; therefore, how to find the optimized weight vector is a key and challenging problem. Aiming at this optimization problem, this paper proposes an ensemble feature selection approach based on genetic algorithm (EFS-BGA). After each base feature selector generates a feature subset, the EFS-BGA method obtains the optimized weight of each feature subset through genetic algorithm, which is different from traditional genetic algorithm directly processing single features. We divide the EFS-BGA algorithm into two types. The first is a complete ensemble feature selection method; based on the first, we further propose the selective EFS-BGA model. After that, through mathematical analysis, we theoretically explain why weight adjustment is an optimization problem and how to optimize. Finally, through the comparative experiments on multiple data sets, the advantages of the EFS-BGA algorithm in this paper over the previous ensemble feature selection algorithms are explained in practice. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

Smart metering in electricity markets offers an opportunity to explore more diverse tariff structures. In this article residential electricity demand and the System Marginal Price of Ireland's Single Electricity Market are simulated to estimate the wholesale risk associated with possible tariffs. A genetic algorithm (GA) with a stochastic fitness function is proposed to search for time-of-use tariffs that minimise wholesale risk to the supplier in residential markets. Alternative search algorithms and fitness functions are investigated in detail, as well as trade-offs in GA and simulation parameter settings. [ABSTRACT FROM AUTHOR]

Power distribution systems (PDS) in urban areas suffer from different types of problems. One such major problem is accidental or scheduled interruption. In electrical networks, effects of interruptions are usually quantified using a set of reliability indices, namely the System Average Interruption Frequency Index and the System Average Interruption Duration Index. Installation cost (fixed cost) and cost due to temporary and/or permanent faults during interruptions (variable cost) are also major issues to be considered while achieving a cost efficient, fault-tolerant PDS. Formalization of an optimization problem that jointly minimizes the afore-mentioned reliability indices as well as the cost of a PDS by optimal allocation of different protective devices and switches has always been a challenging task. This paper presents a hybrid single as well as joint-objective function optimization technique to minimize different reliability indices (mixed integer minimization problems), as well as the operational cost of a PDS in urban areas. In the proposed technique, two well-known meta-heuristic search techniques, namely genetic algorithms (GA) and ant colony optimization (ACO), have been hybridized after modifying different participating operators. The effectiveness of the proposed algorithm is examined, and each PDS is tested in a different environment of constrained optimization. In addition, the presented simulation results are compared with existing approaches that solve this problem. The simulation results show the superiority of the proposed hybrid GA-ACO model, as compared to other established heuristic approaches. [ABSTRACT FROM AUTHOR]

K-Means users usually have to decide on the number of clusters and the initial state by themselves. Evolutionary K-Means (EKM), a hybrid algorithm of K-Means and genetic algorithm, solves the problem by choosing the two parameters automatically through partition evolution; however, the final partition obtained often doesn't meet users' expectations. As a solution to this problem, we suggest using background knowledge for enhancing clustering quality and propose a semi-supervised approach that incorporates instance level constraints into the objective function of EKM. Firstly, we define Constrained Silhouette Index (CS) for data instances, which decreases the silhouette index of the instance having violated constraints. Then, we present two weighted approaches to extend the influence of constraints beyond the level of instance for evaluating the quality of a cluster or a partition. To evaluate the performance of CS in guiding EKM algorithms, we combine the two types of CS with F-EAC algorithm, and get two constrained EKM algorithms, which are named as CEAC1 and CEAC2, respectively. Experimental results on two artificial datasets and eight UCI datasets suggest a few constraints are often powerful enough to improve the accuracy of labelling instances and choosing K, and more constraints may improve the performance even more. [ABSTRACT FROM AUTHOR]