Search

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

In this paper, a novel hybrid optimization algorithm (H-HHONM) which combines the Nelder-Mead local search algorithm with the Harris hawks optimization algorithm is proposed for solving real-world optimization problems. This paper is the first research study in which both the Harris hawks optimization algorithm and the H-HHONM are applied for the optimization of process parameters in milling operations. The H-HHONM is evaluated using well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a milling manufacturing optimization problem is solved for investigating the performance of the H-HHONM. Additionally, the salp swarm algorithm is used to solve the milling problem. The results of the H-HHONM for design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, teaching learning-based optimization algorithm, cuckoo search algorithm, multi-verse optimization algorithm, Harris hawks optimization optimization algorithm, gravitational search algorithm, ant lion optimizer, moth-flame optimization algorithm, symbiotic organisms search algorithm, and mine blast algorithm. The results show that H-HHONM is an effective optimization approach for optimizing both design and manufacturing optimization problems.

In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency. Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina

In this paper, we address a collaborative learning team formation problem in higher education environments. This problem considers a grouping criterion successfully evaluated in a wide variety of higher education courses and training programs. To solve the problem, we propose a hybrid evolutionary algorithm based on adaptive mutation and crossover processes. The behavior of these processes is adaptive according to the diversity of the evolutionary algorithm population. These processes are meant to enhance the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on ten different data sets, and then, is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm considerably outperforms the previous algorithm. Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina Fil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentina

Malaria is one of the world's most widespread parasitic diseases. The parasitic protozoans of the genus Plasmodium have developed resistance to several antimalarial drugs. Some patients are therefore infected by two or more strains with different levels of antimalarial drug sensitivity. We previously developed a model to estimate the drug concentration ( IC 50 ) that inhibits 50% of the growth of the parasite isolated from a patient infected with one strain. We propose here a new Two-Slopes model for patients infected by two strains. This model involves four parameters: the proportion of each strain and their IC50, and the sigmoidicity parameter. To estimate the parameters of this model, we have developed a new algorithm called PGBO (Population Genetics-Based Optimizer). It is based on the Metropolis-Hasting algorithm and is implemented in the statistical software R. We performed a simulation study and defined three evaluation criteria to evaluate its properties and compare it with three other algorithms (Gauss-Newton, Levenberg-Marquardt, and a simulated annealing). We also evaluated it using in vitro data and three ex vivo datasets from the French Malaria Reference Center.Our evaluation criteria in the simulation show that PGBO gives good estimates of the parameters even if the concentration design is poor. Moreover, our algorithm is less sensitive than Gauss-Newton algorithms to initial values. Although parameter estimation is good, interpretation of the results can be difficult if the proportion of the second strain is close to 0 or 1. For these reasons, this approach cannot yet be implemented routinely. HighlightsWe model the antimalarial sensitivity in a blood sample with two strains of parasite.We develop a Metropolis-Hasting algorithm to estimate parameters of the model.We compare our estimation results with three other algorithms.We evaluate our algorithm on a simulation study, on in vitro and ex vivo data.For poor concentration design, our algorithm gives accurate results.

Genetic Algorithm is an algorithm based on population and many optimization problems are solved with this method, successfully. With increasing demand for computer attacks, security, efficient and reliable Internet has increased. Cryptographic systems have studied the science of communication is hidden, and includes two case categories including encryption, password and analysis. In this paper, several code analyses based on genetic algorithms, tabu search and simulated annealing for a permutation of encrypted text are investigated. The study also attempts to provide and to compare the performance in terms of the amount of check and control algorithms and the results are compared.

Conference of 2016 IEEE International 3D Systems Integration Conference, 3DIC 2016 ; Conference Date: 8 November 2016 Through 11 November 2016; Conference Code:129057; International audience; CoolCubeTM is a monolithic 3D (M3D) technology offering a vertical density of integration 20 times higher than face to face copper hybrid bonding (F2F Cu-Cu), thanks to ultra-thin Monolithic Inter-tier Vias (MIVs). In this work, we propose a new partitioning tool exploiting this characteristic for 2-tier Cell-on-Cell ICs before placement. It is based on a fast and iterative algorithm that explores the space of solutions and minimizes the estimated cost of wires with balanced area between tiers without limiting the number of MIVs. A mathematical formulation of the 3D partitioning problem and a comprehensive framework, based on simulated annealing (SA) algorithm coupled with a dedicated cost function, are detailed and compared with Min-Cut (MC) partitions commonly used. It appears that our solution can decrease the estimated total cost of wires by 41% and 45% for the LDPC and FFT/AES units. It also reduces the total cost of wires by 30% to 44% compared to the MC algorithm for the same units and with no significant increase in runtime.

In this paper, we perform Simulated Annealing (SA) algorithm for optimizing size of a PV/wind integrated hybrid energy system with battery storage. The proposed methodology is a heuristic approach which uses a stochastic gradient search for the global optimization. In the study, the objective function is the minimization of the hybrid energy system total cost. And the decision variables are PV size, wind turbine rotor swept area and the battery capacity. The optimum result obtained by SA algorithm is compared with our former study's result. Consequently, it is come up with that the SA algorithm gives better result than the Response Surface Methodology (RSM). The case study is realized for a campus area in Turkey. © 2009 Elsevier Ltd.

We propose a strategy of using a stochastic optimization technique, namely, simulated annealing to design optimum laser pulses (both IR and UV) to achieve greater fluxes along the two dissociating channels (O-18 + (OO16)-O-16 and O-16 + (OO18)-O-16) in (OOO18)-O-16-O-16 molecule. We show that the integrated fluxes obtained along the targeted dissociating channel is larger with the optimized pulse than with the unoptimized one. The flux ratios are also more impressive with the optimized pulse than with the unoptimized one. We also look at the evolution contours of the wavefunctions along the two channels with time after the actions of both the IR and UV pulses and compare the profiles for unoptimized (initial) and optimized fields for better understanding the results that we achieve. We also report the pulse parameters obtained as well as the final shapes they take. (C) 2015 AIP Publishing LLC.

In manufacturing systems, optimal buffer allocation has a considerable impact on capacity improvement. This study presents a simulation optimization procedure to solve the buffer allocation problem in a heat exchanger production plant so as to improve the capacity of the system. For optimization, three metaheuristic-based search algorithms, i.e. a binary-genetic algorithm (B-GA), a binary-simulated annealing algorithm (B-SA) and a binary-tabu search algorithm (B-TS), are proposed. These algorithms are integrated with the simulation model of the production line. The simulation model, which captures the stochastic and dynamic nature of the production line, is used as an evaluation function for the proposed metaheuristics. The experimental study with benchmark problem instances from the literature and the real-life problem show that the proposed B-TS algorithm outperforms B-GA and B-SA in terms of solution quality. © 2014 Taylor and Francis.

This work presents a novel framework for the aerodynamic design and optimization of blades for small horizontal axis wind turbines (WT). The framework is based on a state-of-the-art blade element momentum model, which is complemented with the XFOIL 6.96 software in order to provide an estimate of the sectional blade aerodynamics. The framework considers an innovative nested-hybrid solution procedure based on two metaheuristics, the virtual gene genetic algorithm and the simulated annealing algorithm, to provide a near-optimal solution to the problem. The objective of the study is to maximize the aerodynamic efficiency of small WT (SWT) rotors for a wide range of operational conditions. The design variables are (1) the airfoil shape at the different blade span positions and the radial variation of the geometrical variables of (2) chord length, (3) twist angle, and (4) thickness along the blade span. A wind tunnel validation study of optimized rotors based on the NACA 4-digit airfoil series is presented. Based on the experimental data, improvements in terms of the aerodynamic efficiency, the cut-in wind speed, and the amount of material used during the manufacturing process were achieved. Recommendations for the aerodynamic design of SWT rotors are provided based on field experience. © 2014 José F. Herbert-Acero et al.

In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that concerns with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent time increments among disassembly tasks. Due to the high complexity of the SDDLBP, there is currently no known way to optimally solve even moderately sized instances of the problem; therefore an efficient methodology based on the simulated annealing is proposed to solve the SDDLBP. © IFAC.

This paper describes one approach to the design of reinforced concrete (RC) bridge piers, using a three-hybrid multiobjective simulated annealing (SA) algorithm with a neighborhood move based on the mutation operator from the genetic algorithms (GAs), namely MOSAMO1, MOSAMO2 and MOSAMO3. The procedure is applied to three objective functions: the economic cost, the reinforcing steel congestion and the embedded CO2 emissions. Additional results for a random walk and a descent local search multi-objective algorithm are presented. The evaluation of solutions follows the Spanish Code for structural concrete. The methodology was applied to a typical bridge pier of 23.97 m in height. This example involved 110 design variables. Results indicate that algorithm MOSAMO2 outperforms other algorithms regarding the definition of Pareto fronts. Further, the proposed procedure will help structural engineers to enhance their bridge pier designs. © Zhejiang University and Springer-Verlag Berlin Heidelberg 2012., Project supported by the Spanish Ministry of Science and Innovation (No. BIA2011-23602), and the European Community with the European Regional Development Fund (FEDER), Spain

This paper introduces a new hybrid optimisation approach by combining immune algorithm and simulated annealing algorithm. The main goal of the present research is to develop a novel optimisation approach for the solution of engineering optimisation problems. To demonstrate the effectiveness and robustness of the proposed approach, it is applied to engineering optimisation problems including the multi-pass turning problem taken from the literature and a shape design optimisation problem from automotive industry. Compared with other existing approaches in the literature, the proposed approach is very useful for optimal design and manufacturing.

Bu çalışma, 01-04 Haziran 2008 tarihleri arasında Bursa[Türkiye]’da düzenlenen 4. Automotive Technology Conference on Vehicle Design, Development and Manufacturing (OTEKON 08 - VDDM)’da bildiri olarak sunulmuştur. An approach based on simulated annealing algorithm and heuristic method is presented as an efficient means of scheduling the manufacturing operations of virtual cellular manufacturing systems in the automotive industry. The objectives are to minimise the total weighted tardiness of the production schedule and to minimise the total materials travelling distance. A two-stage approach is employed for scheduling the manufacturing operations. In the first stage, the simulated annealing algorithm is applied to get the optimal schedule. In the second stage, a heuristic approach that was presented by Mak et al. (2007) is employed with some adjustments to minimise the total materials travelling distance. Examples are introduced to evaluate the performance of the present approach and to illustrate how the approach is employed to tackle scheduling problems. The results show that the approach is quite successful and can be used for scheduling the virtual manufacturing cells for the production of parts in case of frequently changing demands.