Stochastic Optimization Approaches for Solving Sudoku

In this paper the Sudoku problem is solved using stochastic search techniques and these are: Cultural Genetic Algorithm (CGA), Repulsive Particle Swarm Optimization (RPSO), Quantum Simulated Annealing (QSA) and the Hybrid method that combines Genetic Algorithm with Simulated Annealing (HGASA). The results obtained show that the CGA, QSA and HGASA are able to solve the Sudoku puzzle with CGA finding a solution in 28 seconds, while QSA finding a solution in 65 seconds and HGASA in 1.447 seconds. This is mainly because HGASA combines the parallel searching of GA with the flexibility of SA. The RPSO was found to be unable to solve the puzzle.Comment: 13 page

Shrimp closed-loop supply chain network design

none3Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP-hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings.openMosallanezhad B.; Hajiaghaei-Keshteli M.; Triki C.Mosallanezhad, B.; Hajiaghaei-Keshteli, M.; Triki, C

Shrimp closed-loop supply chain network design

Recent developments in food industries have attracted both academic and industrial practitioners. Shrimp as a well-known, rich, and sought-after seafood, is generally obtained from either marine environments or aquaculture. Central prominence of Shrimp Supply Chain (SSC) is brought about by numerous factors such as high demand, market price, and diverse fisheries or aquaculture locations. In this respect, this paper considers SSC as a set of distribution centers, wholesalers, shrimp processing factories, markets, shrimp waste powder factory, and shrimp waste powder market. Subsequently, a mathematical model is proposed for the SSC, whose aim is to minimize the total cost through the supply chain. The SSC model is NP-hard and is not able to solve large-size problems. Therefore, three well-known metaheuristics accompanied by two hybrid ones are exerted. Moreover, a real-world application with 15 test problems are established to validate the model. Finally, the results confirm that the SSC model and the solution methods are effective and useful to achieve cost savings

Processing of Spatio-Temporal Hybrid Search Algorithms in Heterogenous Environment Using Stochastic Annealing NN Search

In spatio-temporal database the mixed regions are present in a random manner. The existing work produces the result to create new research opportunities in the area of adaptive and hybrid SLS algorithms. This algorithm develops initialization algorithms which are used only for the homogenous environment. Most current approaches assume, as we have done here, only the homogenous mixtures. Approach: To overcome the above issue, we are going to implement a new technique termed Stochastic Annealing Nearest Neighbor Search using hybrid search algorithms (SANN- HA) for spatio-temporal heterogeneous environment to retrieve the best solution. It provides enhanced fits for definite run length distributions, and would be useful in other contexts as well. Results: Performance of Stochastic Annealing Nearest Neighbor Search using hybrid search algorithms is to discover different sub explanations using different mixture of algorithms in terms of run length distribution and average time for execution based on data objects. Conclusion: It considers the problem of retrieving the high quality solution from the heterogeneous environment. An analytical and empirical result shows the better result with the efficient hybrid search algorithms of our proposed SANN scheme

A hybrid grey wolf optimizer for process planning optimization with precedence constraints

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.Web of Science1423art. no. 736

Adaptiver Suchansatz zur multidisziplinären Optimierung von Leichtbaustrukturen unter Verwendung hybrider Metaheuristik

Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. Choosing the best design and production process in the development period is the most significant link in the automobile production chain. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables. This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material. An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results. Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool. The ability of such tool to present the optimum results in multi-objective problems is improved by using some user-selected fitness functions. Finally, an exemplary sub-assembly made of sheet metal parts from a car body is optimized to enhance both, static load case and crashworthiness.Die Automobilindustrie hat in den letzten Jahren unter dem Druck von Umweltvorschriften, Sicherheitsanforderungen und wettbewerbsfähigem Markt neue Wege auf dem Gebiet der Technologien eröffnet. Leichtbau gilt als eine der innovativsten und offenkundigsten Lösungen, um Umwelt- und Sicherheitsziele zu wettbewerbsfähigen Preisen zu erreichen. Die Wahl des besten Designs und Verfahrens für Produktionen in der Entwicklungsphase ist der wichtigste Ring der Automobilproduktionskette. Um unzählige Produktionsziele zu erreichen, müssen zahlreiche Design- und Prozessparameter bewertet werden. Die Anzahl und Variation der Lösungen und Ziele sowie einige Einschränkungen wie Fertigungsbeschränkungen, finanzielle Grenzen und Fristen beeinflussen die Auswahl einer guten Kombination von Variablen. In dieser Studie werden strukturelle Optimierungswerkzeuge für aus Blech gefertigte Baugruppen, z. Karosserie, basierend auf Parametrisierung und Bewertung von Lösungen in CAD bzw. CAE. Diese Methodik konzentriert sich auf die Lösungen, die dazu führen, dass die richtige Menge an leichtem / festem Material an der richtigen Stelle der Struktur verwendet wird, anstatt vollständig ersetzt zu werden. Eine adaptive Hybrid-Metaheuristik soll verhindern, dass alle Faktoren, die Bedrohungsoptimierungstools in einem lokalen Minimum konvergieren, anstelle eines globalen Optimums. Das Auffinden des globalen Optimums wird durch einige explorative und ausbeuterische Such Heuristiken gewährleistet. Die Zuverlässigkeit, Genauigkeit und Geschwindigkeit des vorgeschlagenen Algorithmus wird mit ähnlichen Algorithmen in akademischen Optimierungsproblemen validiert und führt zu respektablen Ergebnissen. Da Strukturen möglicherweise einem weiten Bereich von Lastfällen unterliegen, z. statische, zyklische, dynamische, Temperatur usw. Möglichkeit der multidisziplinären Optimierung wurde in Optimierungswerkzeugen bereitgestellt. Um die nichtlineare Reaktion von Zielen zu überwinden und um den hohen Zeitverbrauch von FEM-Analysen in Absturzereignissen zu bewältigen, könnte ein Ersatzmodell vom Benutzer verwendet werden. Die Fähigkeit von Optimierungswerkzeugen, optimale Ergebnisse bei Problemen mit mehreren Zielsetzungen zu präsentieren, wird durch die Verwendung einiger vom Benutzer ausgewählten Fitnessfunktionen verbessert. Eine Unterbaugruppe aus Blechteilen, die zur Automobilkarosserie gehören, ist optimiert, um beide zu verbessern; statischer Lastfall und Crashsicherheit

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.This work was carried out within the project "TexBoost: less Commodities more Specialities" reference POCI-01-0247-FEDER-024523, co-funded by Fundo Europeu de Desenvolvimento Regional (FEDER), through Portugal 2020 (P2020)

Application of modern metaheuristic algorithms in optimization ofprocess planning

Optimizacija tehnoloških procesa pripada grupi kompleksnih problema kod kojih je akcenat stavljen na određivanje redosleda zahvata obrade i optimalnu selekciju varijanti tehnoloških resursa među kojima se izdvajaju mašine alatke, rezni alati i smerovi prilaza reznih alata. Optimizacija se vrši minimiziranjem funkcije cilja koja je formulisana na bazi troškova, odnosno vremena realizacije zahvata obrade delova prizmatičnog i rotacionog oblika. Pored toga, pravila i odnosi prethođenja među tipskim tehnološkim oblicima i zahvatima obrade formiraju tzv. ograničenja prethođenja koja omogućavaju pronalaženje izvodljivih rešenja usklađenih sa tehnološkim zahtevima razmatranih mašinskih delova. Predloženi metaheuristički algoritmi za rešavanje ovog problema su algoritmi vrane, sivog vuka i grbavog kita. Pored teorijske analize ovih metoda izvršena je verifikacija njihovih performansi na šest različitih eksperimentalnih studija.Optimization of process planning belongs to the group of complex problems in which the emphasis is placed on determining the sequence of machining operations and the optimal selection of variants of technological resources such as machines, cutting tools and tool approach directions. Optimization is achieved by minimizing the objective function which is formulated on the basis of cost and time required for performing all the operations for prismatic or rotational parts. In addition, precedence rules and relationships among features and machining operations define so called precedence constraints which aid in finding feasible solutions that are complied with technological requirements of considered mechanical parts. The proposed metaheuristic algorithms for solving this problem are crow search optimization, grey wolf optimizer and whale optimization algorithm. Beside the theoretical analysis of these methods, verification of their performances was done on six different experimental studies

Optimal economic operation of electric power systems using genetic based algorithms.

The thesis explores the potential of Genetic Algorithms (GAs) for optimising the operation of electric power systems. It discusses methods which have resulted in significant direct cost saving in operating an electric power system. In particular, the thesis demonstrates the simple search procedure and the powerful search ability of Gas in multi-modal, multi-objective problems, which are resisted by the most well known conventional techniques. Special emphasis has been given to the effectiveness of the enhanced genetic based algorithms and the importance of sophisticated problem structures. Finally, the feasibility and suitability of genetic based algorithms for power system optimisations are verified on a real power supply system. The basic requirement in operating a power system is to ensure that the whole system is run at the minimum possible cost, and the lowest possible pollution level, while reliability and security are maintained. These requirements have resulted in a wide range of power system optimisation problems. In this work, a selection of problems concerning operation economy, security and environmental impact have been dealt with by Genetic Algorithms. These problems are in order of increasing complexity as the project progresses: they range from static problems to dynamic problems, single objective to multi-objectives, softly constrained problems to harshly constrained problems, simple problem structure to more rigorous problem structure. Despite the diversity, GAs consistently produce solutions comparable to conventional techniques over the wide range of problem spectrum. It has been clearly demonstrated that a sophisticated problem structure can bring significant financial benefits in system operation, it has however added further complexity to the problem, where the best result may only be sought from the genetic based algorithms. The enhancements of Genetic Algorithms have been investigated with the aim of further improving the quality and speed of the solution. They have been enhanced in two levels: the first is to develop advanced genetic strategies, and this is subsequently refined by choosing optimal parameter values to further improve the strategies. The outcome of the study clearly indicates that genetic based algorithms are very attractive techniques for solving the ever more complicated optimisations of electric power systems. The basic requirement in operating a power system is to ensure that the whole system is run at the minimum possible cost, and the lowest possible pollution level, while reliability and security are maintained. These requirements have resulted in a wide range of power system optimisation problems. In this work, a selection of problems concerning operation economy, security and environmental impact have been dealt with by Genetic Algorithms. These problems are in order of increasing complexity as the project progresses: they range from static problems to dynamic problems, single objective to multi-objectives, softly constrained problems to harshly constrained problems, simple problem structure to more rigorous problem structure. Despite the diversity, GAs consistently produce solutions comparable to conventional techniques over the wide range of problem spectrum. It has been clearly demonstrated that a sophisticated problem structure can bring significant financial benefits in system operation, it has however added further complexity to the problem, where the best result may only be sought from the genetic based algorithms. The enhancements of Genetic Algorithms have been investigated with the aim of further improving the quality and speed of the solution. They have been enhanced in two levels: the first is to develop advanced genetic strategies, and this is subsequently refined by choosing optimal parameter values to further improve the strategies. The outcome of the study clearly indicates that genetic based algorithms are very attractive techniques for solving the ever more complicated optimisations of electric power systems