This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

SCA: A Sine Cosine Algorithm for solving optimization problems

Thank you for reading experimental and quasi experimental designs for research. Maybe you have knowledge that, people have search numerous times for their favorite readings like this experimental and quasi experimental designs for research, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop.

Experimental And Quasi Experimental Designs For Research

International Society for Technology in Education.

A survey on new generation metaheuristic algorithms

Thank you for reading software product line engineering foundations principles and techniques. Maybe you have knowledge that, people have look numerous times for their favorite books like this software product line engineering foundations principles and techniques, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their desktop computer.

Software Product Line Engineering Foundations Principles And Techniques

This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed.

/pdf/metaheuristic-optimization-frameworks-a-survey-and-1j1za7wup9.pdf

Metaheuristic optimization frameworks: a survey and benchmarking

The automated analysis of feature models is a flourishing research topic that has called the attention of both researchers and practitioners during the last two decades. During this time, the number of tools and techniques enabling the analysis of feature models has increased and also their complexity. In this scenario, the lack of specific testing mechanisms to assess the correctness and good performance of analysis tools is becoming a major obstacle hindering the development of tools and affecting their quality and reliability. In this paper, we present BeTTy, a framework for BEnchmarking and TesTing on the analYsis of feature models. Among other features, BeTTy enables the automated detection of faults in feature model analysis tools. Also, it supports the generation of motivating test data to evaluate the performance of analysis tools in both average and pessimistic cases. Part of the functionality of the framework is provided through a web-based interface facilitating the random generation of both classic and attributed feature models.

/pdf/betty-benchmarking-and-testing-on-the-automated-analysis-of-1yca8kh2g8.pdf

BeTTy: benchmarking and testing on the automated analysis of feature models

http://www.isa.us.es/sites/default/files/benavides15-jss.pdf

An assessment of search-based techniques for reverse engineering feature models

Multi-objective test case prioritization in highly configurable systems

In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the creation of QoS-aware composite web services. Finding the set of service providers that result in the best QoS is a NP-hard optimization problem. This paper presents QoS-Gasp, a metaheuristic algorithm for performing QoS-aware web service composition at runtime. QoS-Gasp is an hybrid approach that combines GRASP with Path Relinking. For the evaluation of our approach we compared it with related metaheuristic algorithms found in the literature. Experiments show that when results must be available in seconds, QoS-Gasp improves the results of previous proposals up to 40%. Beside this, QoS-Gasp found better solutions than any of the compared techniques in a 92% of the runs when results must be available in 100ms; i.e., it provides compositions with a better QoS, implying cost savings, increased availability and reduced execution times for the end-user.

http://www.isa.us.es/sites/default/files/Solving-QosAwareCWS-using-GRASPwithPR.pdf

José Antonio Parejo

Papers

Metaheuristic optimization frameworks: a survey and benchmarking

BeTTy: benchmarking and testing on the automated analysis of feature models

An assessment of search-based techniques for reverse engineering feature models

Multi-objective test case prioritization in highly configurable systems

QoS-aware web services composition using GRASP with Path Relinking