Differential Evolution Simulated Annealing Research Articles

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).

Near-surface velocity inversion from Rayleigh wave dispersion curves based on a differential evolution simulated annealing algorithm

The utilization of urban underground space in a smart city requires an accurate understanding of the underground structure. As an effective technique, Rayleigh wave exploration can accurately obtain information on the subsurface. In particular, Rayleigh wave dispersion curves can be used to determine the near-surface shear-wave velocity structure. This is a typical multiparameter, high-dimensional nonlinear inverse problem because the velocities and thickness of each layer must be inverted simultaneously. Nonlinear methods such as simulated annealing (SA) are commonly used to solve this inverse problem. However, SA controls the iterative process though temperature rather than the error, and the search direction is random; hence, SA always falls into a local optimum when the temperature setting is inaccurate. Specifically, for the inversion of Rayleigh wave dispersion curves, the inversion accuracy will decrease with an increasing number of layers due to the greater number of inversion parameters and large dimension. To solve the above problems, we convert the multiparameter, high-dimensional inverse problem into multiple low-dimensional optimizations to improve the algorithm accuracy by incorporating the principle of block coordinate descent (BCD) into SA. Then, we convert the temperature control conditions in the original SA method into error control conditions. At the same time, we introduce the differential evolution (DE) method to ensure that the iterative error steadily decreases by correcting the iterative error direction in each iteration. Finally, the inversion stability is improved, and the proposed inversion method, the block coordinate descent differential evolution simulated annealing (BCDESA) algorithm, is implemented. The performance of BCDESA is validated by using both synthetic data and field data from western China. The results show that the BCDESA algorithm has stronger global optimization capabilities than SA, and the inversion results have higher stability and accuracy. In addition, synthetic data analysis also shows that BCDESA can avoid the problems of the conventional SA method, which assumes the S-wave velocity structure in advance. The robustness and adaptability of the algorithm are improved, and more accurate shear-wave velocity and thickness information can be extracted from Rayleigh wave dispersion curves. • We convert the high-dimensional inverse problem into multiple low-dimensional optimizations to improve accuracy by using BCD. • We ​use the error as control conditions different from that in SA to avoid the inversion falling into local optima. • We introduce DE to ensure that the iterative error steadily decreases by correcting the iterative error direction.

Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.

An improved differential evolution algorithm for solving a distributed assembly flexible job shop scheduling problem

The single-factory manufacturing is gradually transiting to the multi-factory collaborative production with the globalization. The decentralization of resources and the heterogeneity of the production modes make it difficult to solve this kind of problem. Therefore, the distributed assembly flexible job shop scheduling problem (DAFJSP) is studied. DAFJSP can be decomposed into several flexible job shop scheduling problems and several single machine factory scheduling problems. To begin with, a mixed integer linear programming model for the DAFJSP is formulated to minimize the earliness/tardiness and the total cost simultaneously. Then, an improved differential evolution simulated annealing algorithm (IDESAA) is proposed. The balanced scheduling algorithm is designed to trade off the two objectives. Two crossover and mutation operators are designed. Due to its strong robustness, simulated annealing is integrated to local search the best Pareto solutions. The greedy idea combined with the Non-Dominated Sorted selection is employed to select the offspring. Finally, comprehensive experiments are conducted and the results show that the proposed algorithm can solve DAFJSP effectively and efficiently.

Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm’s fast convergence and simulated annealing algorithm’s jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.

Differential evolution-simulated annealing for multiple sequence alignment

Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.

Integrated petrophysical log evaluation for coalbed methane in the Hancheng area, China

In order to investigate coalbed physical parameters and gas bearing properties, based on coal core experiments, a nonlinear comprehensive model estimating coal properties and gas content is established. Three-dimensional numerical simulation of the dual laterolog response of a coalbed fracture is carried out, and a dual laterolog fast computation model for predicting fracture porosity is established. The results show that the study area mainly developed lean coal and meagre coal. The coalbed is rich in gas. Ash content, volatile dry ash-free basis, true density and fixed carbon show good correlation. The logging responses of the coalbed have distinct characteristics; however, the responses highly depend on coal composition and gas content. The simulated annealing differential evolution (SADE) neural network is adopted to predict coal composition and gas content. Numerical analysis of the dual laterolog responses of the coal fractures shows that as coal matrix resistivity increases, the dual laterolog apparent resistivity increases accordingly. For a coalbed with low-angle fractures, deep and shallow resistivity show little difference; however, for a coalbed with high-angle fractures, its dual laterolog shows an obvious positive difference. Fracture porosity and dual laterolog apparent conductivity presents a linear relationship. According to coal matrix resistivity distribution and fracture development, a fast computation model to predict fracture porosity is established based on the dual laterolog. Field data show that the predicted coal composition and gas content from the SADE algorithm agree with that of coal core analysis, and the calculated fracture porosity based on the dual laterolog matches the coal core's macroscopic description.

Fitness based Differential Evolution

Differential Evolution (DE) is a well known and simple population based probabilistic approach for global optimization. It has reportedly outperformed a few Evolutionary Algorithms and other search heuristics like Particle Swarm Optimization when tested over both benchmark and real world problems. But, DE, like other probabilistic optimization algorithms, sometimes exhibits premature convergence and stagnates at suboptimal point. In order to avoid stagnation behavior while maintaining a good convergence speed, a new position update process is introduced, named fitness based position update process in DE. In the proposed strategy, position of the solutions are updated in two phases. In the first phase all the solutions update their positions using the basic DE and in the second phase, all the solutions update their positions based on their fitness. In this way, a better solution participates more times in the position update process. The position update equation is inspired from the Artificial Bee Colony algorithm. The proposed strategy is named as Fitness Based Differential Evolution ( $$FBDE$$ ). To prove efficiency and efficacy of $$FBDE$$ , it is tested over 22 benchmark optimization problems. A comparative analysis has also been carried out among proposed FBDE, basic DE, Simulated Annealing Differential Evolution and Scale Factor Local Search Differential Evolution. Further, $$FBDE$$ is also applied to solve a well known electrical engineering problem called Model Order Reduction problem for Single Input Single Output Systems.