Showing papers on "Simulated annealing published in 2020"

High Performance Computing for Cyber Physical Social Systems by Using Evolutionary Multi-Objective Optimization Algorithm

Abstract: Cyber-physical social systems (CPSS) is an emerging complicated topic which is a combination of cyberspace, physical space, and social space Many problems in CPSS can be mathematically modelled as optimization problems, and some of them are multi-objective optimization (MOO) problems (MOPs) In general, the MOPs are difficult to solve by traditional mathematical programming methods High performance computing with much faster speed is required to address these issues In this paper, a kind of high performance computing approaches, evolutionary multi-objective optimization (EMO) algorithms, is used to deal with these MOPs A floorplanning case study is presented to demonstrate the feasibility of our proposed approach B*-tree and a multistep simulated annealing (MSA) algorithm are cooperatively used to solve this case As per experimental results for this case, the proposed method is well capable of searching for feasible floorplan solutions, and it can reach 7444 percent (268/360) success rates for floorplanning problems

A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails

Abstract: In this paper, a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO) and simulated annealing for the purpose of accelerating its glo...

Truck-drone team logistics: A heuristic approach to multi-drop route planning

Abstract: Recently there have been significant developments and applications in the field of unmanned aerial vehicles (UAVs). In a few years, these applications will be fully integrated into our lives. The practical application and use of UAVs presents several problems that are of a different nature to the specific technology of the components involved. Among them, the most relevant problem deriving from the use of UAVs in logistics distribution tasks is the so-called “last mile” delivery. In the present work, we focus on the resolution of the truck-drone team logistics problem. The problems of tandem routing have a complex structure and have only been partially addressed in the scientific literature. The use of UAVs raises a series of restrictions and considerations that did not appear previously in routing problems; most notably, aspects such as the limited power-life of batteries used by the UAVs and the determination of rendezvous points where they are replaced by fully-charged new batteries. These difficulties have until now limited the mathematical formulation of truck-drone routing problems and their resolution to mainly small-size cases. To overcome these limitations we propose an iterated greedy heuristic based on the iterative process of destruction and reconstruction of solutions. This process is orchestrated by a global optimization scheme using a simulated annealing (SA) algorithm. We test our approach in a large set of instances of different sizes taken from literature. The obtained results are quite promising, even for large-size scenarios.

An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Abstract: Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris’ Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon’s statistical test ( $P$ -value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm.

Abstract: In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

Deploying Data-intensive Applications with Multiple Services Components on Edge

Abstract: In the information age, the amount of data is huge which shows an exponential growth. In addition, most services of application need to be interdependent with data, cause that they can be executed under the driven data. In fact, such a data-intensive service deployment requires a good coordination among different edge servers. It is not easy to handle such issues while data transmission and load balancing conditions change constantly between edge servers and data-intensive services. Based on the above description, this paper proposes a Data-intensive Service Edge deployment scheme based on Genetic Algorithm (DSEGA). Firstly, a data-intensive edge service composition and an edge server model will be generated based on a graph theory algorithm, then five algorithms of Genetic Algorithm (GA), Simulated Annealing Algorithm (SA), Ant Colony Algorithm (ACO), Optimized Ant Colony Algorithm (ACO_v) and Hill Climbing will be respectively used to obtain an optimal deployment scheme, so that the response time of the data-intensive edge service deployment reaches a minimum under storage constraints and load balancing conditions. The experimental results show that the DSEGA algorithm can get the shortest response time among the service, data components and edge servers.

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a "Factory-in-a-Box"

Abstract: The “factory-in-a-box” concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The “factory-in-a-box” planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of sub-assembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the “factory-in-a-box” supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the “factory-in-a-box” supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of “factory-in-a-box”, demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well.

Monte-Carlo Imaging for Optical Interferometry

Abstract: We present a flexible code created for imaging from the bispectrum and visibility-squared. By using a simulated annealing method, we limit the probability of converging to local chi-squared minima as can occur when traditional imaging methods are used on data sets with limited phase information. We present the results of our code used on a simulated data set utilizing a number of regularization schemes including maximum entropy. Using the statistical properties from Monte-Carlo Markov chains of images, we show how this code can place statistical limits on image features such as unseen binary companions.

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

Abstract: This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robot...

Electric fish optimization: a new heuristic algorithm inspired by electrolocation

Abstract: Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA; vortex search, VS; genetic algorithm, GA; differential evolution, DE; particle swarm optimization, PSO; and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors.

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Abstract: Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning

Abstract: We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.

Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems.

Abstract: Optimization problems are ubiquitous in scientific research, engineering, and daily lives. However, solving a complex optimization problem often requires excessive computing resource and time and faces challenges in easily getting trapped into local optima. Here, we propose a memristive optimizer hardware based on a Hopfield network, which introduces transient chaos to simulated annealing in aid of jumping out of the local optima while ensuring convergence. A single memristor crossbar is used to store the weight parameters of a fully connected Hopfield network and adjust the network dynamics in situ. Furthermore, we harness the intrinsic nonlinearity of memristors within the crossbar to implement an efficient and simplified annealing process for the optimization. Solutions of continuous function optimizations on sphere function and Matyas function as well as combinatorial optimization on Max-cut problem are experimentally demonstrated, indicating great potential of the transiently chaotic memristive network in solving optimization problems in general.

Physics successfully implements Lagrange multiplier optimization.

Abstract: Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip-flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

A New Approach to Identifying a Multi-Criteria Decision Model Based on Stochastic Optimization Techniques

Abstract: Many scientific papers are devoted to solving multi-criteria problems. Researchers solve these problems, usually using methods that find discrete solutions and with the collaboration of domain experts. In both symmetrical and asymmetrical problems, the challenge is when new decision-making variants emerge. Unfortunately, discreet identification of preferences makes it impossible to determine the preferences for new alternatives. In this work, we propose a new approach to identifying a multi-criteria decision model to address this challenge. Our proposal is based on stochastic optimization techniques and the characteristic objects method (COMET). An extensive work comparing the use of hill-climbing, simulated annealing, and particle swarm optimization algorithms are presented in this paper. The paper also contains preliminary studies on initial conditions. Finally, our approach has been demonstrated using a simple numerical example.

Application of particle swarm optimization to water management: an introduction and overview.

Abstract: Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall–runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.