Hybrid Simulated Annealing Algorithm Research Articles

A hybrid SATS algorithm based security constrained optimal power flow using FACTS devices

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness.

Solving continuous quadratic programming for the discrete balanced graph partitioning problem using simulated annealing algorithm hybridized local search and multi-start algorithms

In a discrete balanced graph partitioning problem (DBGPP), a simple undirected weighted graph in that both vertices and edges are weighted is given. The task is dividing vertices into two disjoint subsets such that the sum of the weights of cut edges is minimized and the sum of the weights of vertices in each subset must equal or be as close as possible to each other. Here in order to solve the DBGPP, we first transform the problem into continuous quadratic programming and then show that the new problem has a binary solution, which is the optimal solution for the DBGPP. We also give necessary and sufficient conditions of continuous quadratic programming to identify stationary and local optimal solutions. In addition, we propose a local search to find a binary solution of the continuous quadratic programming. An approximated solution of the DBGPP is obtained using a hybrid simulated annealing algorithm. In our proposed algorithm, the structure of the objective function in a continues problem is considered as the evaluator function. Due to the Dolan–Moré performance profiles and the non-parametric statistical one-sided Wilcoxon signed rank test, we demonstrate the efficiency of our proposed approach in comparison with other available methods.

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

A hybrid metaheuristic algorithm for scheduling iron ore reclaiming in ports

ABSTRACT This work deals with a scheduling problem of reclaimers. This problem involves a set of ships to be loaded with predefined stockpiles. These stockpiles are unloaded into ship holds, and each hold must store a predefined product and quantity of iron ore. Stockpiles are stored in storage yards, which have reclaimers to reclaim iron ore from stockpiles and send it to berthed ships. The objective is to schedule the operations of reclaimers to minimize the sum of ships' berthing times. A hybrid algorithm, combining the multi-start, GRASP and variable neighbourhood descent procedures, is proposed to treat instances of any scale of the problem. It was tested in real instances, and its results were compared with the hybrid simulated annealing algorithm from the literature and a fast simulation algorithm that emulates operators' decision-making. The proposed algorithm was the best in all instances, and statistical tests proved its superiority

Economic optimization of fresh logistics pick-up routing problems with time windows based on gray prediction

The rapid development of modern cold chain logistics technology has greatly expanded the sales market of agricultural products in rural areas. However, due to the uncertainty of agricultural product harvesting, relying on the experience values provided by farmers for vehicle scheduling can easily lead to low utilization of vehicle capacity during the pickup process and generate more transportation cost. Therefore, this article adopts a non-linear improved grey prediction method based on data transformation to estimate the pickup demand of fresh agricultural products, and then establishes a mathematical model that considers the fixed vehicle usage cost, the damage cost caused by non-linear fresh fruit and vegetable transportation damage and decay rate, the cooling cost generated by refrigerated transportation, and the time window penalty cost. In order to solve the model, a hybrid simulated annealing algorithm integrating genetic operators was designed to solve this problem. This hybrid algorithm combines local search strategies such as the selection operator without repeated strings and the crossover operator that preserves the best substring to improve the algorithm’s solving performance. Numerical experiments were conducted through a set of benchmark examples, and the results showed that the proposed algorithm can adapt to problem instances of different scales. In 50 customer examples, the difference between the algorithm and the standard value in this paper is 2.30%, which is 7.29% higher than C&S. Finally, the effectiveness of the grey prediction freight path optimization model was verified through a practical case simulation analysis, achieving a logistics cost savings of 9.73%.

Resource allocation and route planning under the collection price uncertainty for the biomass supply chain

The escalating severity of global warming has drawn worldwide attention to ecological problems. Nevertheless, with the introduction of environmental policies, biomass resources have been effectively developed as a renewable energy source. This paper investigates an advanced biomass supply chain design wherein biomass resources are initially converted into bio-oil through widely distributed fast pyrolysis facilities, and are subsequently transported to a centralised biorefinery for further refining into biofuels. This novel biomass supply chain addressed three key issues: (1) The number of fast pyrolysis facilities, (2) The allocation of resources, and (3) The routes of resources transport. In respect of these problems, a two-stage stochastic mixed integer programming model is established to minimise the total cost of the biomass supply chain considering the uncertainty collection price of fast pyrolysis facilities. A hybrid simulated annealing algorithm which incorporates the sample average approximation method is proposed to solve the stochastic model and is effectiveness for large-scale examples. Finally, a sensitivity analysis is performed using the proposed algorithm and the results show that the proposed stochastic model outperforms the deterministic model under uncertain collection price. The model allows optimising the biomass supply chain economic performances and minimise financial risk on investment by determining the fast pyrolysis facility locations, reasonable resource allocation and optimal transport routes under uncertain collection price.

Continuous Space Wireless Communication Tower Placement by Hybrid Simulated Annealing

Wireless communication tower placement arises in many real-world applications. This paper investigates a new emerging wireless communication tower placement problem, namely, continuous space wireless communication tower placement. Unlike existing wireless communication tower placement problems, which are discrete computational problems, this new wireless communication tower placement problem is a continuous space computational problem. In this paper, we formulate the new wireless communication tower placement problem and propose a hybrid simulated annealing algorithm that can take advantage of the powerful exploration capacity of simulated annealing and the strong exploitation capacity of a local optimization procedure. We also demonstrate through experiments the effectiveness of this hybridization technique and the good performance and scalability of the hybrid simulated annulling in this paper.

Accurate solution of the Index Tracking problem with a hybrid simulated annealing algorithm

An actively managed portfolio almost never beats the market in the long term. Thus, many investors often resort to passively managed portfolios whose aim is to follow a certain financial index. The task of building such passive portfolios aiming also to minimize the transaction costs is called Index Tracking (IT), where the goal is to track the index by holding only a small subset of assets in the index. As such, it is an NP-hard problem and becomes unfeasible to solve exactly for indices with more than 100 assets. In this work, we present a novel hybrid simulated annealing method that can efficiently solve the IT problem for large indices and is flexible enough to adapt to financially relevant constraints. By tracking the S&P-500 index between the years 2011 and 2018 we show that our algorithm is capable of finding optimal solutions in the in-sample period of past returns and can be tuned to provide optimal returns in the out-of-sample period of future returns. Finally, we focus on the task of holding an IT portfolio during one year and rebalancing the portfolio every month. Here, our hybrid simulated annealing algorithm is capable of producing financially optimal portfolios already for small subsets of assets and using reasonable computational resources, making it an appropriate tool for financial managers.

Optimal allocation of enterprise marketing resources based on hybrid parallel genetic algorithm and simulated annealing algorithm

Abstract Aiming at minimizing the use of marketing resources, this article establishes the mathematical model of marketing resources allocation, designs the algorithm of marketing resources allocation, and compares the examples. An improved heuristic algorithm considering tilt angle matching is proposed and used as a local search algorithm for enterprise marketing resources. We design an innovative optimization strategy that incorporates the concept of tilt angle matching to enhance the local search efficiency of enterprise marketing resource allocation. In addition, we have introduced a novel parallel grouping genetic algorithm (PGGA), which utilizes grouping coding and exon crossover to further enhance the search and optimization efficiency of the solution. PGGA is improved by using adaptive parameters to form IPGGA, which improves the efficiency and convergence speed of enterprise marketing resource allocation. The annealing function of the simulated annealing algorithm is improved, and a model is constructed to solve the problem of enterprise marketing resource allocation. Simulated annealing algorithm is introduced to solve the problem of marketing resource allocation, and the framework of simulated annealing algorithm is analyzed. To solve the problem of fast decay rate of simulated annealing algorithm, Doppler effect function is used to optimize the algorithm. This article mainly uses qualitative and quantitative analysis methods to conduct in-depth research on enterprise marketing resource allocation. It focuses on the planning and allocation of enterprise marketing resources. Through IPGGA-ISAA research and analysis of all kinds of data of enterprise marketing resources, the present situation, efficiency, and main problems of enterprise marketing resources allocation are discussed and analyzed more deeply. Compared with other algorithms, IPGGA-ISAA can better analyze the causes of problems and provide better marketing resource allocation schemes for enterprises.

Construction of Cloud Computing Task Scheduling Model Based on Simulated Annealing Hybrid Algorithm

Construction of Cloud Computing Task Scheduling Model Based on Simulated Annealing Hybrid Algorithm

Minimizing Virtual Machine Live Migration Latency for Proactive Fault Tolerance Using an ILP Model With Hybrid Genetic and Simulated Annealing Algorithms

Minimizing Virtual Machine Live Migration Latency for Proactive Fault Tolerance Using an ILP Model With Hybrid Genetic and Simulated Annealing Algorithms

A novel hybrid simulated annealing algorithm for colored bottleneck traveling salesman problem

In the fields, such as intelligent transport and multiple tasks cooperation, many real-world problems can be modeled by colored bottleneck traveling salesman problem (CBTSP). Since CBTSP has been proved belong to the NP-hard problems, the population-based algorithms, such as genetic algorithm (GA), hill climbing GA (HCGA) and simulated annealing GA (SAGA), have been used for solving it. However, in term of convergence speed and solution quality, their performances are limited. Moreover, some intelligent algorithms still have the problems such as “black box problem” or lacking theoretical support. Aiming at these problems, this paper proposes a new hybrid simulated annealing algorithm with generating neighborhood solution and crossover operator (GCSA) for CBTSP. In the iterative process of GCSA, the gene coding (dual-chromosome coding) is used to code feasible solutions, and the improved generating neighborhood solution and crossover operator by ITÖ lemma (Wiener process) are used to explore and exploit new unknown region. During the process, generating neighborhood solution and crossover operator are both controlled by the activity intensity based on Wiener process. The experiments show that GCSA can demonstrate an improvement over the state-of-the-art algorithms for this problem.

Heterogeneous Mission Planning for Multiple UAV Formations via Metaheuristic Algorithms

Unmanned aerial vehicle (UAV) formation has been widely applied in various aspects, both for military and civil purposes. In this paper, multiple UAV formations are deployed to perform different types of tasks, and the heterogeneous mission planning problem is studied. The problem is divided into two subproblems, i.e., task assignment and path planning. First, the task assignment is formulated into a combinatorial optimization problem. Different from the existing studies, appropriate UAVs need to be selected to build the formation and perform specific types of tasks. To obtain the task assignment scheme, a hybrid genetic and simulated annealing (HGSA) algorithm is proposed. In this algorithm, a feasible solution is guaranteed by the designed task-based strategy, and the diversity of solutions is ensured by the group-based solution remaining (GBSR) approach. In the path planning problem, an obstacle avoidance-enabled consensus (OAEC) algorithm is developed to form the UAV formation, which extends the application of the standard consensus algorithm. A multi-step particle swarm optimization (MPSO) algorithm is combined with the consensus algorithm to generate a path for the UAV formation to reach each task point. The conflict among multiple UAV formations is resolved by adjusting their departure time. Furthermore, the task assignment scheme is modified by utilizing real path information on the flying distance between the UAVs and the task points. The simulation results demonstrate that the HGSA algorithm can build the UAV formations and assign the tasks to them while satisfying all the complicated constraints. The advantages of the OAEC algorithm and the MPSO algorithm in path planning are verified by comparison with relevant algorithms.

A hybrid simulated annealing and variable neighborhood search algorithm for the close-open electric vehicle routing problem

AbstractElectric Vehicles (EVs) are the future of transportation, but due to their battery and charging technology they cannot yet directly replace traditional vehicles. Nonetheless, EVs are a great option for city-logistics, due to the small distances and their zero local emissions. In this paper, a novel variant of the Electric Vehicle Routing Problem (EVRP), called Close-Open EVRP (COEVRP), is presented. It considers ending EV trips at Charging Stations, as opposed to other EVRP variants that only allow for en-route charging. This new variant follows a traditional routing scheme, allowing EVs to recharge only at the end of their route. The objective is to minimize energy consumption, as well as the number of vehicles. The energy consumption function takes into account the weight of the transported items. A mathematical formulation for the problem is presented and small instances were solved using a commercial solver. To solve larger instances, a hybrid metaheuristic combining Simulated Annealing and Variable Neighborhood Search algorithm was employed and thoroughly tested.

A sustainable and resilient supply chain (RS-SCM) by using synchronisation and load-sharing approach: application in the oil and gas refinery

Nowadays sustainable and resilient supply chain management (R&S-SCM) is an interesting and in the meantime vital problem that has grabbed the attention of many researchers. This study is an effort to investigate the simultaneous design of sustainable and resilient flow in the supply chain. To achieve a sustainable and resilient supply chain, there is a wide variety of strategies depending on their impacts on different industries and the features of their supply chain. The fuzzy QFD method is designed to select the most appropriate strategy in the study. Two approaches are used for this purpose. Early planning in sustainable mode is formulated assuming that all members of the network are healthy and final planning in the reactive approach to destruction which formulates the return to the original state in the shortest possible time, cost and scenario-based status. A hybrid priority-based Genetic Algorithm (pb-GA) and simulated annealing algorithm (SA) is developed in two phases to find the optimal solutions. Response Surface Methodology (RSM) method is used to adjust significant parameters for the algorithm. Several test problems are generated showing that the proposed metaheuristic algorithm can find good solutions in reasonable time spans.

A hybrid simulated annealing algorithm to estimate a better upper bound of the minimal total cost of a transportation problem with varying demands and supplies

Minimizing the total cost of transportation of a homogeneous product from multiple sources to multiple destinations when demand at each source and supply at each destination are deterministic and constant is commonly addressed in the literature. However, in practice, demand and supply may fluctuate within a certain range due to variations of the global economy. Subsequently, finding the upper bound of the minimal total cost of this transportation problem with varying demands and supplies (TPVDS) is NP hard. Yet, bounding the minimal total cost is of prime importance for financial sustainability. Although the lower bound of the minimal total cost can be methodologically attained, determining the exact upper bound is challenging. Herein, we demonstrate that existing methods may underestimate this upper minimal total cost bound. We therefore propose an alternative efficient and robust method that is based on the hybridization of simulated annealing and steepest descent. We provide theoretical evidence of its good performance in terms of solution quality and prove its superiority in comparison to existing techniques. We further validate its performance on benchmark and newly generated instances. Finally, we exemplify its utility on a real-world TPVDS.

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

In this paper, the problem of finding an assignment of “n” surgeries to be presented in one of “m” identical operating rooms (ORs) or machines as the surgical case scheduling problem (SCSP) is proposed. Since ORs are among NP-hard optimization problems, mathematical and metaheuristic methods to address OR optimization problems are used. The job or surgical operation ordering in any OR is a permanent part of all sequencing and scheduling problems. The transportation times between ORs are defined based on the type of surgical operations and do not depend on distance, so there is no surgical operation waiting time for transferring. These problems are called no-wait open-shop scheduling problems (NWOSP) with transportation times. The transportation system for the problems is considered a multi-transportation system with no limitation on the number of transportation devices. Accordingly, this study modeled a novel combined no-wait open-shop surgical case scheduling problem (NWOSP-SCSP) with multi-transportation times for the first time to minimize the maximum percentile of makespan for OR as a single objective model. A mixed-integer linear program (MILP) with small-sized instances is solved. In addition to the small-sized model, a novel metaheuristic based on a hybrid simulated annealing (SA) algorithm to solve large-sized problems in an acceptable computational time is suggested, considering the comparison of the SA algorithm and a new recommended heuristic algorithm. Then, the proposed hybrid SA and SA algorithms are compared based on their performance measurement. After reaching the results with a numerical analysis in Nova Scotia health authority hospitals and health centers, the hybrid SA algorithm has generated significantly higher performance than the SA algorithm.

Two‐Layer Location‐Routing Problem Based on Heuristic Hybrid Algorithm

Location‐routing problem (LRP) thoroughly considers location allocation problem (LAP) and vehicle routing problem (VRP) which has been an integral part applied in modern logistics. A number of researchers at home and aboard have put forward their views by establishing fine models. On the basis of studying the previous research results by classification, summary, and comparative analysis, this study hence proposes a new solution‐fuzzy clustering model and algorithm to resolve two‐layer location‐routing problem based on a heuristic hybrid algorithm: Designing a hybrid genetic and simulated annealing algorithm (GASA) to optimize the initial value of the fuzzy C‐means clustering algorithm (FCM); considering the roving visit characteristics of vehicles to design the path by employing a special VRP problem—the multiple traveling salesman problem (MTSP). Theoretical analysis and experimental results show that the algorithm used in this study has the advantages of fast convergence speed and less iterations, which significantly improve the quality of the initial solution of FCM in LAP, shorten the vehicle patrol cycle in VRP to a great extent, improve the vehicle utilization, and save the vehicle patrol costs. A specific example is programmed by MATLAB to verify the feasibility of this method.

Slow Heat-Based Hybrid Simulated Annealing Algorithm in Vehicular Ad Hoc Network.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well-suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat-based SA-GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature-based SA-GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA-GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

Graded Optimization of Upper Limb Exoskeleton Parts for Grid Operations

Upper limb exoskeleton is gradually used in industrial production because of its flexibility, safety, and ease of wear. To improve the load reduction effect of the upper limb exoskeleton in power grid operation, this paper first establishes an efficiency optimization model considering the number of gears, transmission ratio, and pressure angle of upper limb exoskeleton transmission joint gears. Further, an exoskeleton load reduction model based on the joint optimization of multiple parameters of the upper limb exoskeleton is constructed by combining the muscle force application fatigue evaluation. For the above model, a hybrid simulated annealing-flash connection process algorithm based on Cubic mapping and golden sine is designed for the solution. To address the shortcomings of the standard LAPO, Cubic mapping, simulated annealing algorithm, and golden sine operator are introduced for improvement, producing high-quality initial solutions through the traversal of Cubic chaotic sequences, hybrid simulated annealing algorithm to enhance the global search capability of the algorithm, and further incorporating golden sine operator to strengthen the local search capability of the algorithm. In the simulation section, the designed Improved Lightning Connection Process Algorithm (ILAPO) is compared horizontally with cutting-edge swarm intelligence algorithms such as LAPO, PSO, and HBA to verify the feasibility of the model in this paper in the exoskeleton domain and the efficiency of the designed algorithm.