Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in ...the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely
is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.
Primal heuristics have become essential components in mixed integer programming (MIP) solvers. Extending MIP-based heuristics, our study outlines generic procedures to build primal solutions in the ...context of a branch-and-price approach and reports on their performance. Our heuristic decisions carry on variables of the Dantzig–Wolfe reformulation, the motivation being to take advantage of a tighter linear programming relaxation than that of the original compact formulation and to benefit from the combinatorial structure embedded in these variables. We focus on the so-called
diving
methods that use reoptimization after each linear programming rounding. We explore combinations with diversification-intensification paradigms such as
limited discrepancy search
,
sub-MIP
,
local branching
, and
strong branching
. The dynamic generation of variables inherent to a column generation approach requires specific adaptation of heuristic paradigms. We manage to use simple strategies to get around these technical issues. Our numerical results on generalized assignment, cutting stock, and vertex-coloring problems set new benchmarks, highlighting the performance of diving heuristics as generic procedures in a column generation context and producing better solutions than state-of-the-art specialized heuristics in some cases.
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete ...problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
Optimization and decision making problems in various fields of engineering have a major impact in this current era. Processing time and utilizing memory is very high for the currently available data. ...This is due to its size and the need for scaling from zettabyte to yottabyte. Some problems need to find solutions and there are other types of issues that need to improve their current best solution. Modelling and implementing a new heuristic algorithm may be time consuming but has some strong primary motivation - like a minimal improvement in the solution itself can reduce the computational cost. The solution thus obtained was better. In both these situations, designing heuristics and meta-heuristics algorithm has proved it’s worth. Hyper heuristic solutions will be needed to compute solutions in a much better time and space complexities. It creates a solution by combining heuristics to generate automated search space from which generalized solutions can be tuned out. This paper provides in-depth knowledge on nature-inspired computing models, meta-heuristic models, hybrid meta heuristic models and hyper heuristic model. This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain. Various traditional algorithms and new generation meta heuristic algorithms has also been explained for giving readers a better understanding.
•We identify, classify, and analyze 15 multi-attribute vehicle routing problems.•We analyze in detail 64 of the most efficient heuristics for these problems.•We identify winning strategies for ...designing effective heuristics for MAVRP’s.
The attributes of vehicle routing problems are additional characteristics or constraints that aim to better take into account the specificities of real applications. The variants thus formed are supported by a well-developed literature, including a large variety of heuristics. This article first reviews the main classes of attributes, providing a survey of heuristics and meta-heuristics for Multi-Attribute Vehicle Routing Problems (MAVRP). It then takes a closer look at the concepts of 64 remarkable meta-heuristics, selected objectively for their outstanding performance on 15 classic MAVRP with different attributes. This cross-analysis leads to the identification of “winning strategies” in designing effective heuristics for MAVRP. This is an important step in the development of general and efficient solution methods for dealing with the large range of vehicle routing variants.
Design theories provide explicit prescriptions, such as principles of form and function, for constructing an artifact that is designed to meet a set of defined requirements and solve a problem. ...Design theory generation is increasing in importance because of the increasing number and diversity of problems that require the participation and proactive involvement of academic researchers to build and test artifact-based solutions. Heuristic search involves alternating between structuring the problem at hand and generating new artifact design components, whereas heuristic synthesis involves different ways of thinking, including reflection and learning and forms of reasoning, that complement the use of heuristics for theorizing purposes. The authors illustrate the effectiveness of our heuristic theorizing framework through a detailed example of a multiyear design science research program in which we proactively generated a design theory for solving problems in the area of intelligent information management and so-called big data in the finance domain.
We recently introduced a research program on how firms can effectively capture fleeting opportunities using heuristics. Heuristics, we advocate, are the essence of strategy, especially in ...unpredictable markets where opportunities are often numerous, fast moving, and uncertain. Our emphasis on heuristics invites comparison with prominent research programs in cognitive psychology. We address this opportunity by comparing our "simple rules" heuristics approach with "heuristics-and-biases" and "fast-and-frugal" heuristics research. Collectively, the three approaches offer a rich understanding of heuristics.
Does cooperating require the inhibition of selfish urges? Or does "rational" self-interest constrain cooperative impulses? I investigated the role of intuition and deliberation in cooperation by ...meta-analyzing 67 studies in which cognitive-processing manipulations were applied to economic cooperation games (total N = 17,647; no indication of publication bias using Egger's test, Begg's test, or p-curve). My meta-analysis was guided by the social heuristics hypothesis, which proposes that intuition favors behavior that typically maximizes payoffs, whereas deliberation favors behavior that maximizes one's payoff in the current situation. Therefore, this theory predicts that deliberation will undermine pure cooperation (i.e., cooperation in settings where there are few future consequences for one's actions, such that cooperating is not in one's self-interest) but not strategic cooperation (i.e., cooperation in settings where cooperating can maximize one's payoff). As predicted, the meta-analysis revealed 17.3% more pure cooperation when intuition was promoted over deliberation, but no significant difference in strategic cooperation between more intuitive and more deliberative conditions.
The blocking flowshop scheduling problem with makespan criterion has important applications in a variety of industrial systems. Heuristics that explore specific characteristics of the problem are ...essential for many practical systems to find good solutions with limited computational effort. This paper first presents two simple constructive heuristics, namely weighted profile fitting (wPF) and PW, based on the profile fitting (PF) approach of McCormick et al. Sequencing in an assembly line with blocking to minimize cycle time. Operations Research 1989;37:925–36 and the characteristics of the problem. Then, three improved constructive heuristics, called PF-NEH, wPF-NEH, and PW-NEH, are proposed by combining the PF, wPF, and PW with the enumeration procedure of the Nawaz–Enscore–Ham (NEH) heuristic A heuristic algorithm for the
m-machine,
n-job flow shop sequencing problem. OMEGA-International Journal of Management Science 1983;11:91–5 in an effective way. Thirdly, three composite heuristics i.e., PF-NEH
LS, wPF-NEH
LS, and PW-NEH
LS, are developed by using the insertion-based local search method to improve the solutions generated by the constructive heuristics. Computational simulations and comparisons are carried out based on the well-known flowshop benchmarks of Taillard Benchmarks for basic scheduling problems. European Journal of Operation Research 1993;64:278–85 that are considered as blocking flowshop instances. The results show that the presented constructive heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics by a considerable margin. In addition, 17 new best-known solutions for Taillard benchmarks with large scale are found by the presented heuristics.
► The paper studies the blocking flowshop scheduling problem with makespan criterion. ► Some novel heuristics are presented including five constructive heuristics and three composite ones. ► Computational and statistical analyses show the superiority of the presented approaches. ► 17 new best-known solutions for Taillard benchmarks with large scale are found by the presented heuristics.
Summary
Beyond meeting power demand, switching to solar energy especially solar photovoltaic (PV) offers many advantages like modularity, minimal maintenance, pollution free, and zero noise. Yet, its ...cell modeling is critical in design, simulation analysis, evaluation, and control of solar PV system; most importantly to tap its maximum potential. However, precise PV cell modeling is complicated by PV nonlinearity, presence of large unknown model parameter, and absence of a unique method. Since number of model parameters involved is directly related to model accuracy, and efficiency; determination of its values assume high priority. Besides, application of meta‐heuristic algorithms via numerical extraction is popular as it suits for any PV cell/module types and operating conditions. However, existence of many algorithms have drawn attention toward assessment of each method based on its merits, demerits, suitability/ability to parameter estimation problem, and complexity involved. Hence, few authors reviewed the subject of PV model parameter estimation. But existing reviews focused on comparative analysis of analytical and meta‐heuristic approaches, analysis of models, and application of meta‐heuristic methods for model parameter extraction. Thus, lack a comprehensive analysis on methods based on different objective function, assessment on environmental conditions, and cumulative analysis on selective set of algorithm based on efficiency. Therefore, this work reviews optimization algorithms presented for parameter estimation focusing on (a) objective function used, (b) modeling type, (c) algorithm employed for parameter extraction, and (d) PV technology. Further, provides a comprehensive assessment on various modules types used for validation, comparisons made with methods, advantages and disadvantages associated with each method with respect to parameter estimation platform, critical analysis on each method at STC, and varying irradiance conditions. In addition, a critical evaluation on specific set of algorithm based on objective function values is also carried out. Thus explores and display the characteristics of various techniques related to PV cell modeling and serve to be a single reference for researchers working in the field of PV parameter estimation.
Graphical explains about cell modeling and its parameters can be estimated via analytical and meta‐heuristic methods; irrespective of the method, both the them intend to estimate the parameters using mathematical equations or by following iterative steps.
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