Genetic Algorithm And Simulated Annealing Research Articles

Low Latency Aware Fog Nodes Placement in Internet of Things Service Infrastructure

Fog Computing extends storage and computation resources closer to end-devices. In several cases, the Internet of Things (IoT) applications that are time-sensitive require low response time. Thus, reducing the latency in IoT networks is one of the essential tasks. To this end, fog computing is developed with a motive for the data production and consumption to always be within proximity; therefore, the fog nodes must be placed at the edge of the network, which is near the end devices, such that the latency is minimized. The optimal location selection for fog node placement within a network out of a very large number of possibilities, such as minimize latency, is a challenging problem. So, it is a combinatorial optimization problem. Hard combinatorial optimization problems (NP-hard) involve huge discrete search spaces. The fog node placement problem is an NP-hard problem. NP-hard problems are often addressed by using heuristic methods and approximation algorithms. Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore in principle, any metaheuristic can be used to solve them. To resolve this, meta-heuristic-based methods is proposed. We apply the Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to design fog node placement algorithms. Genetic Algorithm is observed to give better solutions. Since Genetic Algorithm may get stuck in local optima, Hybrid Genetic Algorithm, and Simulated Annealing (GA-SA), Hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) were compared with GA. By extensive simulations, it is observed that hybrid GA-SA-based for node placement algorithm outperforms other baseline algorithms in terms of response time for the IoT applications.

A hybrid framework based on genetic algorithm and simulated annealing for RNA structure prediction with pseudoknots

RNA structure prediction with pseudoknots is an NP-complete problem, in which an optimal RNA structure with minimum energy is to be computed. In past decades, several methods have been developed to predict RNA structure with pseudoknots. Among them, metaheuristic approaches have proven to be beneficial for predicting long RNA structure in a very short time. In this paper, we have used two metaheuristic algorithms; Genetic Algorithm (GA) and Simulated Annealing (SA) for predicting RNA secondary structure with pseudoknots. We have also applied a combination of these two algorithms as GA-SA where GA is used for a global search and SA is used for a local search, and conversely SA-GA, where SA is used for a global search and GA is used for a local search. Four different energy models have been applied to calculate the energy of RNA structure. Five datasets, constructed from the RNA STRAND and Pseudobase++ database, have been used in the algorithms. The performances of the algorithms have been compared with several existing metaheuristic algorithms. Here we have obtained that the combination of GA and SA (GA-SA) gives better results than GA, SA and SA-GA algorithms and all other four state-of-art algorithms on all datasets.

Heterogeneous network selection optimization algorithm based on a Markov decision model

A network selection optimization algorithm based on the Markov decision process (MDP) is proposed so that mobile terminals can always connect to the best wireless network in a heterogeneous network environment. Considering the different types of service requirements, the MDP model and its reward function are constructed based on the quality of service (QoS) attribute parameters of the mobile users, and the network attribute weights are calculated by using the analytic hierarchy process (AHP). The network handoff decision condition is designed according to the different types of user services and the time-varying characteristics of the network, and the MDP model is solved by using the genetic algorithm and simulated annealing (GA-SA), thus, users can seamlessly switch to the network with the best long-term expected reward value. Simulation results show that the proposed algorithm has good convergence performance, and can guarantee that users with different service types will obtain satisfactory expected total reward values and have low numbers of network handoffs.

Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows

Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.

Simulation and Analysis of Irregular Multicoil B0 Shimming in C-type Permanent Magnets Using Genetic Algorithm and Simulated Annealing

Multicoil (MC) B0 shimming, a new shimming technology developed in the recent years, exhibits unique advantages. Irregularly shaped coils can further improve the shimming effect of the MC, but they increase the difficulty and complexity of MC design. In this paper, the shimming characteristics of MC in C-type permanent magnets are studied. Aiming at improving the efficiency of finding the optimal shape for irregular MC, this paper presents a method that uses genetic algorithm and simulated annealing (GASA) to optimize irregular MC shapes for shimming. By simulating the shimming of the irregular MC of the flat-plate model and shoulder-joint model in C-type permanent magnets, the effects of parameters such as complexity, size, and number of groups of coils are analyzed. The shimming of MC in superconducting magnets and permanent magnets is compared and the optimization efficiencies of the genetic algorithms and genetic–annealing algorithms are compared. The results show that an irregular MC can achieve a better shimming effect for a locally nonuniform B0 field in a sample boundary region of a C-type permanent magnet. GASA is found to be a more stable and highly efficient optimization method.

Based on global optimization method for inter-diffusion analysis of multicomponent melts

Different from the traditional methods to determine the inter-diffusion coefficients, such as the “diffusion path” method and “square root diffusion coefficient” method, this paper proposed a new algorithm, which was based on the genetic algorithm and simulated annealing algorithm of global search feature, to calculate inter-diffusion coefficients for multicomponent systems. Due to this algorithm combines those intelligent algorithm with the Levenberg- Marquardt algorithm, it has both the capability of global search and ensures optimum accuracy. Using this algorithm to optimize the objective function, which is constructed from the diffusion profiles, we can obtain the inter-diffusion coefficients from the optimal solution. In order to test whether the algorithm are easy and reliable for handling the diffusion process of multicomponent melts, this paper analyzes the diffusion couple with normal diffusion situation, uphill diffusion situation, ideal diffusion situation (do not exist random errors) and large noise ratio situation (with random error in the range of 1 at.%), respectively. The results show that the algorithm can be well applied in those situations and gives very good results. Since the solving process of obtaining the inter-diffusion coefficients by the algorithm is only based on a single diffusion profiles, it can significantly reduce the workload. At the same time, because the calculated process does not need to smooth the measured experimental data and determine the gradient or integral from the measured experimental data, this method improves the precision of measurement of the inter-diffusion coefficients. In addition, this method also has high versatility. This paper shows the calculated process in a three-component melt system, and theoretically, this algorithm can be easily extended into four-component melt system and above to determine the inter-diffusion coefficients. Therefore, compared with the traditional methods, for instance, the “diffusion path” method and “square root diffusion coefficient” method, the method proposed in this paper not only has high precision and versatility, but also obviously reduces the workload of measurement experiments.

Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line

This paper focuses on the scheduling of a single vehicle, which delivers parts from a storage centre to workstations in a mixed-model assembly line. In order to avoid part shortage and to cut down total inventory holding and travelling costs, the destination workstation, the part quantity and the departure time of each delivery have to be specified properly according to predetermined assembly sequences. In this paper, an optimisation model is established for the configuration that only one destination workstation is involved within each delivery. Four specific properties of the problem are deduced, then a backward-backtracking approach and a hybrid GASA (genetic algorithm and simulated annealing) approach are developed based on these properties. Both two approaches are applied to several groups of instances with real-world data, and results show that the GASA approach is efficient even in large instances. Furthermore, the existence of feasible solutions (EOFS) is analysed via instances with different problem settings, which are obtained by an orthodox experimental design (ODE). An analysis of variance (ANOVA) shows that the buffer capacity is the most significant factor influencing the EOFS. Besides this, both the assembly sequence length and distances to workstations also have noticeable impacts.

Determining the optimal number of Kanban in multi-products supply chain system

Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.

Optimization of multi-pass milling using parallel genetic algorithm and parallel genetic simulated annealing

This paper presents an approach to select the optimal machining parameters for multi-pass milling. It is based on two recent approaches, genetic algorithm (GA) and simulated annealing (SA), which have been applied to many difficult combinatorial optimization problems with certain strengths and weaknesses. In this paper, a hybrid of GA and SA (GSA) is presented to use the strengths of GA and SA and overcome their weaknesses. In order to improve, the performance of GSA further, the parallel genetic simulated annealing (PGSA) has been developed and used to optimize the cutting parameters for multi-pass milling process. For comparison, conventional parallel GA (PGA) is also chosen as another optimization method. An application example that has been solved previously using the geometric programming (GP) and dynamic programming (DP) method is presented. From the given results, PGSA is shown to be more suitable and efficient for optimizing the cutting parameters for milling operation than GP+DP and PGA.