Hybrid Simulated Annealing Research Articles

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.

Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products

Establishment of aggregation hubs in a supply chain network (SCN) is typically a facility location-allocation (FLA) decision, which is known to be a NP-hard optimization problem. Considering the flow of heterogeneous perishable products, like fresh produce, with different spoilage rates, further increases the complexity of such a problem. This is due to the effect of transportation time and conditions, services provided in the hub, and hub proximity to supply sources, on the quality and quantity of products eventually reaching the demand destinations, and hence on the location-allocation decision. In this paper, this problem is formulated as a mixed integer linear programming (MILP) model that considers a number of problem characteristics simultaneously for the first time, to minimize the transportation, spoilage, processing, and capacity-based hub establishment costs. Due to its complexity, two hybrid algorithms that combine a meta-heuristic with a perishability-modified transportation algorithm, are proposed to solve the problem. The algorithms are based on binary particle swarm optimization (BPSO) and simulated annealing (SA). Taguchi analysis is used to tune the significant parameters of both algorithms considering different problem sizes. Computational analysis is further conducted to evaluate and compare the performances of the algorithms using randomly generated test instances and exact solutions obtained using CPLEX. Results show that while both algorithms are capable of obtaining optimum solutions for most instances, the hybrid BPSO slightly outperforms the hybrid SA in terms of consistency and solution time.

Distribution network reconfiguration and reactive power compensation using a hybrid Simulated Annealing – Minimum spanning tree algorithm

Change of the climate, environmental pollution and lack of classical energy sources forced many countries to address renewables. In modern strategies of smart distribution network reconfiguration should be applied in order to minimize power losses and economic costs. Also, capacitors switching (placement of shunt capacitors) and presence of distribution generation are always incorporated in modern networks. Further, On Load Tap Changer in the supplying substation has to be used. Simulated Annealing and Minimum spanning tree algorithms work separately in order to find the best appropriate solution. The main goals are minimization of power losses and costs. In addition, many constraints are attended to find the hourly reconfiguration and capacitor banks position. Distributed generators involve both wind generators and solar panels. Two methods described in literature are mixed and implemented for locating fixed nodes for distributed generators. Gauss and Weibull changing nodal loads and wind generators output respectively, as well as daily load curves for working and weekend day with insolation of solar panels are included in analysis. Per unit load diagrams are determined according to real one-year measurement results in one substation in Belgrade, Serbia. IEEE 69 bus network is analyzed. The main contribution of exposed research is near optimal solution for wide time range, realistic network operation/planning forecast (stochastic variation of consumption and production on hour basis) with satisfaction of all technical constraints that are minimum and maximum nodal voltages, limiting feeder’s capacities and not-overcompensation of the network, accompanied with elaborate economic background.

Heuristic position allocation methods for forming multiple UAV formations

It is a common action that the unmanned aerial vehicles (UAVs) change the formation during the flight to realize different goals and address an emergency. In this process, the UAVs should determine their positions in the new formation to avoid the collision and reduce the flight time. In this paper, the position allocation problem for multiple UAV formations is studied, and the problem is solved considering the different requirements on offline and online cases. In the offline case, the trajectories of UAVs when forming new formations are calculated by the consensus-based trajectory planning (CBTP) method, in which the transient process is introduced to avoid the collision among UAVs. Then a hybrid genetic and simulated annealing (HGSA) algorithm, which utilizes the framework of genetic algorithm (GA) and the operators in simulated annealing (SA) algorithm, is proposed to obtain the optimal position allocation scheme. The CBTP method is treated as a part of the HGSA algorithm to calculate the optimization index for a specific position allocation scheme. In the online case, a two-step minimal cost increase strategy (MCIS)-based method is developed and the UAVs in each new formation and the allocated position of UAV in the new formation are determined successively. Simulation results demonstrate that the CBTP method can generate the safe trajectories for the UAVs when forming new formations, and the optimal position allocation scheme can be obtained by the HGSA algorithm. The HGSA algorithm performs better than other similar algorithms especially in the complicated situation. The MCIS-based method can output the partially optimized position allocation scheme at short notice, but the real trajectories of UAVs are not considered in the online case to reduce the computation time.

Harmonic Detection Method Based on Particle Swarm Optimization and Simulated Annealing Algorithm of Electrohydraulic Servo System

Due to the comprehensive influence of many nonlinear coupling factors within a system, when the input signal provided by an electrohydraulic servo shaker is sinusoidal, it often leads to the existence of high-order harmonic components of the system, which makes the output servo signal parameters exist extremely serious. Therefore, the detection of harmonics of the electrohydraulic servo shaker has very important application significance. In this paper, by using simulated annealing (SA) based harmonic detection, a kernel function is introduced to study area influence-based particle swarm optimization (PSO). Using a super accurate and fast global convergence brought by the combination of hybrid particle swarm optimization algorithm and simulated annealing algorithm, it can quickly jump out of the trap of traditional local optimization algorithms and a more stable, high-precision, as well as fast global convergence optimal solution can be obtained. Through the detection and simulation of the amplitude and phase of the harmonics in the system, by comparing the PSO-SA detection with PSO detection, it is proved that the PSO-SA algorithm can well satisfy the accuracy of the detection system, which has advantages such as a fast convergence speed, a high search accuracy, etc.; meanwhile, it is simple and easy to implement.

Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm

Cascade refrigeration system (CRS) can meet a wider range of refrigeration temperature requirements and is more energy efficient than single-refrigerant refrigeration system, making it more widely used in low-temperature industry processes. The synthesis of a CRS with simultaneous consideration of heat integration between refrigerant and process streams is challenging but promising for significant cost saving and reduction of carbon emission. This study presented a stochastic optimization method for the synthesis of CRS. An MINLP model was formulated based on the superstructure developed for the CRS, and an optimization framework was proposed, where simulated annealing algorithm was used to evolve the numbers of pressure/temperature levels for all sub-refrigeration systems, and particle swarm optimization algorithm was employed to optimize the continuous variables. The effectiveness of the proposed methodology was verified by a case study of CRS optimization in an ethylene plant with 21.89% the total annual cost saving.

Optimal PID Controllers for AVR Systems Using Hybrid Simulated Annealing and Gorilla Troops Optimization

In the literature, all investigations dealing with regulator design in the AVR loop observe the AVR system as a single input single output (SISO) system, where the input is the generator reference voltage, while the output is the generator voltage. Besides, the regulator parameters are determined by analyzing the terminal generator voltage response for a step change from zero to the rated value of the generator voltage reference. Unlike literature approaches, in this study, tuning of the AVR controllers is conducted while modeling the AVR system as a double input single output (DISO) system, where the inputs are the setpoint of the generator voltage and the step disturbance on the excitation voltage, while the output is the generator voltage. The transfer functions of the generator voltage dependence on the generator voltage reference value and the excitation voltage change were derived in the developed DISO-AVR model. A novel objective function for estimating DISO-AVR regulator parameters is proposed. Also, a novel metaheuristic algorithm named hybrid simulated annealing and gorilla troops optimization is employed to solve the optimization problem. Many literature approaches are compared using different regulator structures and practical limitations. Furthermore, the experimental results of 120 MVA synchronous generators in HPP Piva (Montenegro) are presented to show the drawbacks of the literature approaches that observe generator setpoint voltage change from zero to the rated value. Based on the presented results, the proposed procedure is efficient and strongly applicable in practice.

Non-isothermal pyrolysis of xylan, cellulose and lignin: A hybrid simulated annealing algorithm and pattern search method to regulate distributed activation energies

Biomass transformation into fuels, platform chemicals and materials contributes to developing sustainability and carbon neutralization. Pyrolysis is one of the most important approaches to valorize biomass. The distributed activation energy model (DAEM) is a comprehensive kinetic model for describing biomass pyrolysis kinetics. The estimation of the DAEM parameters is difficult because of the complex structure of the DAEM equation. This work formulated the numerical calculation approach of the DAEM and proposed a hybrid simulated annealing (SA) algorithm and pattern search (PS) method for optimizing the DAEM parameters. The DAEM kinetic parameters were evaluated by simultaneously fitting the experimental kinetic data of cellulose, xylan and lignin pyrolysis at various heating rates with the DAEM using the proposed hybrid optimization method. The results showed that the proposed hybrid optimization method could effectively and accurately determine the DAEM parameters and the DAEM with the optimal parameters could reproduce the experimental kinetic data of xylan, cellulose and lignin pyrolysis very well. The distributed activation energies of xylan, cellulose and lignin predicted by hybrid optimization were centered at 162.8, 222.8 and 236.7 kJ mol−1, respectively, with standard deviations of 4.6, 0.8 and 25.9 kJ mol−1, respectively. The reaction orders of xylan, cellulose and lignin pyrolysis are 1.7, 1.1, and 2.5, respectively.

Design of Anti-Swing PID Controller for Bridge Crane Based on PSO and SA Algorithm

Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms.

Cross-regional manpower scheduling and routing problem with stochastic service times in home health care

At present, home health care services supply is still insufficient and unbalanced in China, and the elderly nursing manpower is particularly scarce. Therefore, it is important to reasonably arrange the existing nursing personnel to make full use of the service capabilities. This paper proposes a mixed integer linear programming (MILP) model for a cross-regional scheduling and routing problem with stochastic service times in the one-day planning horizon. A range of possible realistic constraints are considered, including patient time windows, working time regulations, task importance, and skill matching. Due to the complexity of the problem, a hybrid Simulated Annealing algorithm is introduced to solve the model. Monte Carlo simulation method is used for the uncertainty cost evaluation. The numerical example tests verify the effectiveness of the algorithm proposed. This paper analyzes the advantages and disadvantages of cross-regional scheduling and routing by comparing the empirical experiment results of the cross-regional and independent scheduling of two regions. It is found that in the case of this paper, cross-regional scheduling is generally beneficial to reducing the cost of both care centers and improving the efficiency of resource utilization.

A hybrid particle swarm optimization and simulated annealing algorithm for the job shop scheduling problem with transport resources

This work addresses a variant of the job shop scheduling problem in which jobs need to be transported to the machines processing their operations by a limited number of vehicles. Given that vehicles must deliver the jobs to the machines for processing and that machines need to finish processing the jobs before they can be transported, machine scheduling and vehicle scheduling are intertwined. A coordinated approach that solves these interrelated problems simultaneously improves the overall performance of the manufacturing system. In the current competitive business environment, and integrated approach is imperative as it boosts cost savings and on-time deliveries. Hence, the job shop scheduling problem with transport resources (JSPT) requires scheduling production operations and transport tasks simultaneously. The JSPT is studied considering the minimization of two alternative performance metrics, namely: makespan and exit time. Optimal solutions are found by a mixed integer linear programming (MILP) model. However, since integrated production and transportation scheduling is very complex, the MILP model can only handle small-sized problem instances. To find good quality solutions in reasonable computation times, we propose a hybrid particle swarm optimization and simulated annealing algorithm (PSOSA). Furthermore, we derive a fast lower bounding procedure that can be used to evaluate the performance of the heuristic solutions for larger instances. Extensive computational experiments are conducted on 73 benchmark instances, for each of the two performance metrics, to assess the efficacy and efficiency of the proposed PSOSA algorithm. These experiments show that the PSOSA outperforms state-of-the-art solution approaches and is very robust.

Deep null fixing on optimal compromise among sum and difference patterns of thinned arrays through the Schelkunoff unit circle representation

Difference far-field patterns represent a way for pin-pointing a target in both azimuth and elevation, extremely useful in radar applications. At the present work, an innovative method for synthesizing good compromise solutions among sum and difference patterns providing low complexity of the antenna feeding network for uniform thinned arrays is addressed. This procedure uses a hybrid version of the Simulated Annealing algorithm (hybrid SA) to optimize a cost function of radiation characteristics for both sum and difference patterns as peak directivity and side lobe level (SLL) while fixing deep nulls. In this framework, examples of half-wavelength spaced linear arrays from 40 to 120 elements were analyzed, as well as an extension to planar arrays by means of separable distributions was developed. The performance of the method is analyzed with different examples and its potential outlined, showing the ability of fixing deep nulls in both sum and difference patterns which share the same uniform excitation relative amplitudes.

Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach

This paper proposes a joint-optimization framework for UAV-routing and UAV-route scheduling problems associated with the UAV-assisted delivery system. The mixed-integer linear programming (MILP) models for UAV-routing and UAV-route scheduling problems are proposed considering the effect of incidental processes and the varying payload on travel time. A hybrid genetic and simulated annealing (HGSA) algorithm is proposed for the UAV-routing problem to minimize travel time. In HGSA, genetic algorithm (GA) employs a novel stochastic crossover operator to search for the optimal global position of customers, whereas simulated annealing (SA) utilizes local search operators to avoid the local optima. A UAV-Oriented MinMin (UO-MinMin) algorithm is also proposed to minimize the makespan of the UAV-route scheduling problem. It employs a UAV-oriented view to generate the route-scheduling order with minimal computational efforts without affecting the quality of the makespan. A Monte Carlo simulation-based sensitivity analysis is conducted to evaluate the impact of the hybridization probability of GA and SA in the proposed HGSA algorithm. To assess the performance of the HGSA algorithm, a set P of 24 benchmark instances is adopted and adjusted to meet the constraints of the UAV-Assisted delivery system. The proposed HGSA outperforms the state-of-the-art algorithms such as genetic algorithm (GA), Particle Swarm Optimization & Simulated Annealing algorithm (PSO-SA), Differential Evolution & Simulated Annealing (DE-SA), and Harris-hawks optimization (HHO). For all 24 instances, the aerial routes generated by HGSA have been used to evaluate the effectiveness of the UO-MinMin algorithm for different numbers of UAVs. The proposed UO-MinMin algorithm outperforms the base algorithms such as minimum completion time (MCT) and opportunistic load balancing (OLB).

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

Municipal waste management has become a challenging issue with the rise in urban populations and changes in people’s habits, particularly in developing countries. Moreover, government policy plays an important role associated with municipal waste management. Thus, this research proposes the regional location routing problem (RLRP) model and multi-depot regional location routing problem (MRLRP) model, which are extensions of the location routing problem (LRP), to provide a better municipal waste collection process. The model is constructed to cover the minimum number of depot facilities’ policy requirements for each region due to government policy, i.e., the large-scale social restrictions in each region. The goal is to determine the depot locations in each region and the vehicles’ routes for collecting waste to fulfill inter-regional independent needs at a minimum total cost. This research conducts numerical examples with actual data to illustrate the model and implements a hybrid genetic algorithm and simulated annealing optimization to solve the problem. The results show that the proposed method efficiently solves the RLRP and MRLRP.

Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem

Feature selection is performed to eliminate irrelevant features to reduce computational overheads. Metaheuristic algorithms have become popular for the task of feature selection due to their effectiveness and flexibility. Hybridization of two or more such metaheuristics has become popular in solving optimization problems. In this paper, we propose a hybrid wrapper feature selection technique based on binary butterfly optimization algorithm (bBOA) and Simulated Annealing (SA). The SA is combined with the bBOA in a pipeline fashion such that the best solution obtained by the bBOA is passed on to the SA for further improvement. The SA solution improves the best solution obtained so far by searching in its neighborhood. Thus the SA tries to enhance the exploitation property of the bBOA. The proposed method is tested on twenty datasets from the UCI repository and the results are compared with five popular algorithms for feature selection. The results confirm the effectiveness of the hybrid approach in improving the classification accuracy and selecting the optimal feature subset.

A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2)emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.

Fuzzy Demand Vehicle Routing Problem with Soft Time Windows

Considering the vehicle routing problem with fuzzy demand and fuzzy time windows, a vehicle routing optimization method is proposed considering both soft time windows and uncertain customer demand. First, a fuzzy chance-constrained programming model is established based on credibility theory, minimizing the total logistics cost. At the same time, a random simulation algorithm is designed to calculate the penalty cost of delivery failures caused by demand that cannot be satisfied. In order to overcome the shortcomings of GA, which easily falls into the local optimum in the process of searching, and the slow convergence speed of SA when the population is too large, a hybrid simulated annealing–genetic algorithm is adopted to improve the solution quality and efficiency. Finally, the Solomon standard example is used to verify the effectiveness of the algorithm, and the influence of decision-makers’ subjective cost preference is analyzed.

Toward Energy Footprint Reduction of a Machining Process

In a machining process, proper selection of process plans and cutting parameters can effectively reduce energy consumption and shorten production time. Traditionally, studies on process planning and cutting parameter optimization for energy saving are mostly concentrated on electrical energy consumption. Since the preparation process of cutting tools and cutting fluid consumes a considerable amount of energy, conservation of this part of energy consumption, namely, the embodied energy consumption, will achieve a more energy-efficient machining process. In this article, an integrated model for process planning and cutting parameter optimization is proposed to shorten production time and reduce the energy footprint (namely, electrical energy consumption and embodied energy consumption of cutting tools and cutting fluid) of a machining process. Considering that the optimization of process plan and cutting parameters in an integrated manner is a hybrid programming process, simulated annealing and quantum-behaved particle swarm optimization (SA-QPSO) hybrid algorithm is employed to solve the proposed model. Results of the case study show that: 1) embodied energy consumption of cutting tools and cutting fluid accounts for a nonnegligible proportion of energy footprint of the machining process and 2) there is a tradeoff between energy footprint and production time, and the balance of them is achieved through the proposed optimization approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article, for the first time, to the best of our knowledge, proposes an integrated approach to reduce both electrical and embodied energy consumption of a machining process through optimizing process plan and cutting parameters. Such broader consideration makes this integrated optimization approach more applicable to real industry settings and contributes to the comprehensive improvement of energy efficiency in the machining process. To better use this approach, the following three steps should be highlighted: 1) the energy footprint characteristics of the machining process should be comprehensively analyzed and modeled; 2) the integrated optimization model for minimizing energy footprint and production time needs to cooperate with machining constraints, such as process centralization, machining sequence, and process requirements; and 3) solving the proposed model is a hybrid programming process since there are discrete decision variables and continuous variables. A proper algorithm should be used to solve the proposed model.

A hybrid column generation and simulated annealing algorithm for direct aperture optimization

The purpose of this work was to develop a hybrid column generation (CG) and simulated annealing (SA) algorithm for direct aperture optimization (H-DAO) and to show its effectiveness in generating high quality treatment plans for intensity modulated radiation therapy (IMRT) and mixed photon-electron beam radiotherapy (MBRT). The H-DAO overcomes limitations of the CG-DAO with two features improving aperture selection (branch-feature) and enabling aperture shape changes during optimization (SA-feature). The H-DAO algorithm iteratively adds apertures to the plan. At each iteration, a branch is created for each field provided. First, each branch determines the most promising aperture of its assigned field and adds it to a copy of the current apertures. Afterwards, the apertures of each branch undergo an MU-weight optimization followed by an SA-based simultaneous shape and MU-weight optimization and a second MU-weight optimization. The next H-DAO iteration continues the branch with the lowest objective function value. IMRT and MBRT treatment plans for an academic, a brain and a head and neck case generated using the CG-DAO and H-DAO were compared. For every investigated case and both IMRT and MBRT, the H-DAO leads to a faster convergence of the objective function value with number of apertures compared to the CG-DAO. In particular, the H-DAO needs about half the apertures to reach the same objective function value as the CG-DAO. The average aperture areas are 27% smaller for H-DAO than for CG-DAO leading to a slightly larger discrepancy between optimized and final dose. However, a dosimetric benefit remains. The H-DAO was successfully developed and applied to IMRT and MBRT. The faster convergence with number of apertures of the H-DAO compared to the CG-DAO allows to select a better compromise between plan quality and number of apertures.

Pipeline Network Optimization using Hybrid Algorithm between Simulated Annealing and Genetic Algorithms

The pipeline network is one of the most complex optimization problems consisting of several elements: reservoirs, pipes, valves, etc. The pipeline network is designed to deliver water to consumers by considering the demand and adequate pressure on the water pipe network. The main problem in designing reliable pipelines is the cost. The amount of cost that most influences the design of pipelines is the diameter of the pipe used. Therefore, this study aims to combine (hybrid) simulated annealing algorithm with genetic algorithm to optimize water pipe networks. The simulated annealing algorithm is the main algorithm in finding the optimal cost.Meanwhile, the genetic algorithm will assist in the pipeline update process using the roulette wheel selection. Simulation data is used to test the hybrid algorithm performance compared to the standard simulated annealing algorithm. The results show that the simulated annealing hybrid algorithm is able to get a more optimal cost in designing a water pipe network compared to the standard simulated annealing algorithm. Keywords: Optimization, Epanet 2.0, Simulated Annealing, and Genetic Algorithm