China is a major developing country where farmers account for over 57% of the population. Thus, promoting a rural economy is crucial if the Chinese government is to improve the quality of life of the ...nation as a whole. To frame scientific and effective rural policy or economic plans, it is useful and necessary for the government to predict the income of rural households. However, making such a prediction is challenging because rural households income is influenced by many factors, such as natural disasters. Based on the Grey Theory and the Differential Evolution (DE) algorithm, this study first developed a high-precision hybrid model, DE–GM(1,1) to forecast the per capita annual net income of rural households in China. By applying the DE algorithm to the optimization of the parameter λ, which was generally set equal to 0.5 in GM(1,1), we obtained more accurate forecasting results. Furthermore, the DE–Rolling–GM(1,1) was constructed by introducing the Rolling Mechanism. By analyzing the historical data of per capita annual net income of rural households in China from 1991 to 2008, we found that DE–Rolling–GM(1,1) can significantly improve the prediction precision when compared to traditional models.
► GM(1,1) is optimized using the DE algorithm and Rolling Mechanism. ► DE–Rolling–GM(1,1) combines Grey Theory, Rolling Mechanism and the DE algorithm. ► DE–Rolling–GM(1,1) is an ideal hybrid model for chaotic forecasting problems.
This book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas ...covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in ...time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely ECMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, ECMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the ECMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the ECMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the ECMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the ECMADE once again. The ECMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems.
In this letter, an efficient and versatile method based on algorithm X and differential evolution algorithm is proposed to synthesize irregular modular subarrayed planar phased arrays with exact ...tiling. To alleviate the time-consuming problem of using algorithm X to synthesize large arrays, the proposed method obtains the subarray configuration with exact tiling through twice-partitioning operations. In the first partition process, the planar phased array is divided into several first-level subarrays. In the second partition process, the first-level subarrays are respectively divided into pre-specified irregular modular subarrays, called second-level subarrays. All feasible schemes for dividing the first-level subarrays and second-level subarrays can be obtained by algorithm X. Through the above partition process, the partitioning paradigm can be represented by only a few variables, which can be optimized by swarm intelligence algorithms. In this letter, the variables representing the subarray configuration are optimized by differential evolution algorithm to obtain excellent radiation performances. Three representative numerical examples are presented and discussed to assess the validity, efficiency, and reliability of the proposed method.
This paper proposes a novel wavefront-shaping-based focusing method, by introducing the differential evolution algorithm (DEA), thereby realising a faster convergence rate and improved enhancement ...compared to rival algorithms. Via simulations, we show that our proposed DEA-based approach delivers the best focusing performance irrespective of the influence of noise. Experimental results demonstrate that the DEA boosts the enhancement for an equivalent number of measurements compared with conventional optimisation methods. Furthermore, we reveal the influence of certain DEA parameters, leading to the emergence of many modified DEAs that perform impressively. The proposed DEA-based method simplifies the computational complexity and implementation process of wavefront shaping, offering useful insights for the future study of optimisation algorithms for wavefront shaping, as well as potential for practical applications, such as deep tissue focusing.
•Differential operation and greedy selection criterion are adopted in optimisation.•Computational complexity is reduced and light focusing is easier to implement.•DEA-based focusing always outperforms regardless of the influence of noise.•Mutation strategies and parameters should be tuned for specific characteristics.
•SMA-AGDE method is proposed for solving various optimization problems.•The algorithm performance is verified on CEC’17 benchmark.•The method performance is verified on 3 engineering and 2 ...combinatorial problems.•Efficiency of the proposed method is compared with many metaheuristics.
The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents’ local search, increase the population’s diversity, and help avoid premature convergence. The SMA-AGDE’s performance is evaluated on the CEC’17 test suite, three engineering design problems – tension/compression spring, pressure vessel, and rolling element bearing – and two combinatorial optimization problems – bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The well-studied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based Optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks Optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.
Biomedical ontology is a unified model for describing biomedical knowledge, which can be of help to solve the issues of heterogeneity in different biomedical databases. However, the existing ...biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Evolution Algorithm (EA) is a state-of-the-art methodology for matching ontologies, but two main shortcomings, i.e. the huge memory consumption and long runtime, make it incapable of effectively matching biomedical ontologies. In this work, a novel Adaptive Compact Differential Evolution algorithm (ACDE) is proposed to solve the biomedical ontology matching problem, which utilizes a compact encoding mechanism to save the memory consumption and introduces the compact adaption schemes on control parameters to improve the algorithm’s converging speed. The experiment exploits four biomedical ontology matching tracks, which are provided by the famous Ontology Alignment Evaluation Initiative (OAEI), to test ACDE’s performance. The experimental results show that ACDE can effectively reduce EA-based ontology matcher’s memory consumption and runtime, and its results significantly outperform other EA-based matchers and OAEI’s participants.
Disasters have caused significant losses to humans in the past decades. It is essential to learn about the disaster situation so that rescue works can be conducted as soon as possible. Unmanned ...aerial vehicle (UAV) is a very useful and effective tool to improve the capacity of disaster situational awareness for responders. In the paper, UAV path planning is modelled as the optimization problem, in which fitness functions include travelling distance and risk of UAV, three constraints involve the height of UAV, angle of UAV, and limited UAV slope. An adaptive selection mutation constrained differential evolution algorithm is put forward to solve the problem. In the proposed algorithm, individuals are selected depending on their fitness values and constraint violations. The better the individual is, the higher the chosen probability it has. These selected individuals are used to make mutation, and the algorithm searches around the best individual among the selected individuals. The well-designed mechanism improves the exploitation and maintains the exploration. The experimental results have indicated that the proposed algorithm is competitive compared with the state-of-art algorithms, which makes it more suitable in the disaster scenario.
Two-dimensional metamaterials with patterns of perforations producing auxetic effects can exhibit variable and tailorable deformation mechanisms by varying the distributions of cells with different ...geometry parameters. The local homogenized Poisson’s ratio can be used as a way to establish as a link between local unit cell parameters and global deformations. We propose here a Poisson’s ratio-based unit cell distribution optimization method to design deformation patterns in a perforated structure. A plate-like structure with centresymmetric perforations is here divided into different regions with dissimilar unit cell topologies that possess different homogenized Poisson’s ratio values. All the unit cells belonging to the same region have equal geometry. A differential evolution (DE) algorithm is used to optimize the permutation and combination of the homogenized local Poisson’s ratios of the unit cells regions. A two-dimensional perforated structure that satisfies the required deformation pattern can be obtained by using the proposed method. Simulations and experiments show that the proposed approach can provide controllable shape changes of 2D perforated mechanical metamaterials under uniaxial tensile loading.
