Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

A General Variable Neighborhood Search Algorithm for the No-Idle Permutation Flowshop Scheduling Problem

Abstract: In this study, a general variable neighborhood search (GVNS) is presented to solve no-idle permutation flowshop scheduling problem (NIPFS), where idle times are not allowed on machines. GVNS is a metaheuristic, where inner loop operates a variable neighborhood descend (VND) algorithm whereas the outer loop carries out some perturbations on the current solution. We employ a simple insert and swap moves in the outer loop whereas iterated greedy (IG) and iterated local search (ILS) algorithms are employed in the VND as neighborhood structures. The results of the GVNS algorithm are compared to those generated by the variable iterated greedy algorithm with differential evolution (vIG_DE). The performance of the proposed algorithm is tested on the Ruben Ruiz’ benchmark suite that is presented in http://soa.iti.es/rruiz. Computational results showed that the GVNS algorithm further improved 85 out of 250 best solutions found so far in the literature.

Exploiting Model Morphology for Event-Based Testing

Abstract: Model-based testing employs models for testing. Model-based mutation testing (MBMT) additionally involves fault models, called mutants, by applying mutation operators to the original model. A problem encountered with MBMT is the elimination of equivalent mutants and multiple mutants modeling the same faults. Another problem is the need to compare a mutant to the original model for test generation. This paper proposes an event-based approach to MBMT that is not fixed on single events and a single model but rather operates on sequences of events of length k ≥ 1 and invokes a sequence of models that are derived from the original one by varying its morphology based on k . The approach employs formal grammars, related mutation operators, and algorithms to generate test cases, enabling the following: (1) the exclusion of equivalent mutants and multiple mutants; (2) the generation of a test case in linear time to kill a selected mutant without comparing it to the original model; (3) the analysis of morphologically different models enabling the systematic generation of mutants, thereby extending the set of fault models studied in related literature. Three case studies validate the approach and analyze its characteristics in comparison to random testing and another MBMT approach.

A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems

Abstract: In this paper, a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known, the performance of a DE algorithm depends on the mutation strategy and its control parameters, namely, crossover and mutation rates. For this reason, the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore, a new mutation operator, namely DE/Best to current/l, is proposed in the DE-QL algorithm. In this paper, 57 EDPs provided in “Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results” are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition.

Cloud-based model predictive building thermostatic controls of commercial buildings: Algorithm and implementation

Abstract: The contribution of this paper is in two-folds: 1) If more predictive and intelligent control of the thermostat setpoints with no explicit models of Root Top Units (RTUs) yet with simplistic lumped parameter thermal models of buildings can be effective in reducing a small commercial buildings summer-time peak load while adequately maintaining comfort levels, and 2) how this simplistic indirect control approach to RTUs compare to more sophisticated direct control approaches in terms of peak-load reduction and cost. First, the modelpredictive control approach is presented. Second, the results of cloud-based implementation of the optimization algorithm at the two demonstration commercial buildings owned by General Electric (GE), optimizer characteristics, different set point trajectories and their implication with regards to peak load and comfort, and observations are described. On average, the savings from the indirect optimal control strategy utilized in our approach through a cloud-based control implementation architecture is shown to be comparable to previously stated savings in literature from more sophisticated direct optimal control of RTUs while the comfort levels are the same as the non-optimal strategy or slightly better in some cases.

The meta-geography of the Middle East and North Africa in Turkey’s new geopolitical imagination:

Abstract: This article critically analyses Turkish security discourses connected to the meta-geography of the Broader Middle East and North Africa (BMENA) before and after the developments of the Arab Spring...