Self-adaptive Parameter Research Articles

A nonmonotone flexible filter method for nonlinear constrained optimization

In this paper, we present a flexible nonmonotone filter method for solving nonlinear constrained optimization problems which are common models in industry. This new method has more flexibility for the acceptance of the trial step compared to the traditional filter methods, and requires less computational costs compared with the monotone-type methods. Moreover, we use a self-adaptive parameter to adjust the acceptance criteria, so that Maratos effect can be avoided a certain degree. Under reasonable assumptions, the proposed algorithm is globally convergent. Numerical tests are presented that confirm the efficiency of the approach.

Improved biogeography-based optimization with random ring topology and Powell's method

Biogeography-based optimization (BBO) is a competitive population optimization algorithm based on biogeography theory with inherently insufficient exploration capability and slow convergence speed. To overcome limitations, we propose an improved variant of BBO, named PRBBO, for solving global optimization problems. In PRBBO, a hybrid migration operator with random ring topology, a modified mutation operator, and a self-adaptive Powell's method are rational integrated together. The hybrid migration operator with random ring topology, denoted as RMO, is created by using local ring topology to replace global topology, which can avoid the asymmetrical migration operation and enhance potential population diversity. The self-adaptive Powell's method is amended by using self-adaptive parameters for suiting evolution process to enhance solution precision quickly. Extensive experimental tests are carried out on 24 benchmark functions to show effectiveness of the proposed algorithm. Simulation results were compared with original BBO, ABC, DE, other variants of the BBO, and other state-of-the-art evolutionary algorithms. Finally, the effectiveness of operators on the performance of PRBBO is also discussed.

Self-Adaptive Differential Evolution Hyper-Heuristic with Applications in Process Design

The paper presents a differential evolution (DE)-based hyper-heuristic algorithm suitable for the optimization of mixed-integer non-linear programming (MINLP) problems. The hyper-heuristic framework includes self-adaptive parameters, an e-constrained method for handling constraints, and 18 DE variants as low-level heuristics. Using the proposed approach, we solved a set of classical test problems on process synthesis and design and compared the results with those of several state-of-the-art evolutionary algorithms. To verify the consistency of the proposed approach, the above-mentioned comparison was made with respect to the percentage of convergences to the global optimum (NRC) and the average number of objective function evaluations (NFE) over several trials. Thus, we found that the proposed methodology significantly improves performance in terms of NRC and NFE.

Study on a Novel Data Classification Method Based on Improved GA and SVM Model

Support vector machine(SVM) can effectively solve the classification problem with small samples, nonlinear and high dimensions, but it exits the weak generalization ability and low classification accuracy. So an improved genetic algorithm(IGA) is introduced in order to propose a new classification(IGASVM) method based on combining improved GA and SVM model. In the proposed IGASVM method, the self-adaptive control parameter strategy and improving convergence speed strategy are introduced into the GA to keep the diversity of the population, promptly reflect the premature convergence of the individual and escape from the local optimal solution for improving the search performance. Then the improved GA is used to optimize and determine the parameters of the SVM model in order to improve the learning ability and generalization ability of the SVM model for obtaining new classification (IGASVM) method. Finally, the experiment data is selected to test the effectiveness of the proposed IGASVM method. The experiment results show that the improved GA can effectively optimize and determine the parameters of the SVM model, and the IGASVM method takes on the better learning ability, generalization ability and classification accuracy.

New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter.

The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes self-adaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easy-to-perform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multi-zone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters.

Low speed control and implementation of permanent magnet synchronous motor for mechanical elastic energy storage device with simultaneous variations of inertia and torque

The spiral torsion spring-based mechanical elastic energy storage (MEES) device presented previously with inherent characteristic of simultaneous variations of inertia and torque is disadvantage to be actuated by conventional control method. This study proposes an improved non-linear backstepping control scheme based on combination of recursive least squares (RLS) and differential evolution (DE) optimisation to regulate permanent magnet synchronous motor (PMSM) at a very low speed and control MEES device effectively. For this presented control scheme, first, a single RLS method with forgetting factor is presented to simultaneously estimate the time-varying inertia and torque of MEES device. Second, on the basis of estimation results, an improved non-linear backstepping control algorithm considering parameter variations in derivation process is proposed to drive PMSM to operate at a very low speed. Finally, a self-adaptive parameter DE method is designed to optimise the several control parameters. The performance of the proposed methodology is verified through simulations and experiments.

A novel multistart hyper-heuristic algorithm on the grid for the quadratic assignment problem

Hyper-heuristics introduce novel approaches for solving challenging combinatorial optimization problems by operating over a set of low level (meta)-heuristics. This is achieved by an evolutionary selection mechanism that controls and combines the strengths of the low level (meta)-heuristics. In this study, we propose a high-performance MultiStart Hyper-heuristic algorithm (MSH-QAP) on the grid for the solution of the Quadratic Assignment Problem (QAP). MSH-QAP algorithm makes use of state-of-the-art (meta)-heuristics, Simulated Annealing (SA), Robust Tabu Search (RTS), Ant Colony Optimization (FAnt), and Breakout Local Search (BLS) that have been reported among the best performing algorithms for the solution of difficult QAP instances in standard benchmark libraries. In the first phase of the algorithm, the most appropriate (meta)-heuristic with its near-optimal parameter settings is selected by using a genetic algorithm optimization layer that uses a self-adaptive parameter setting method for the given problem instance. In the second phase, if an optimal solution cannot be found, selected best performing (meta)-heuristic (with its finely adjusted parameter settings) is executed on the grid using parallel processing and performing several multistarts in order to increase the quality of the discovered solution. MSH-QAP algorithm is tested on 134 problem instances of the QAPLIB benchmark and is shown to be able to solve 122 of the instances exactly. The overall deviation for the problem instances is obtained as 0.013% on the average.

Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling

This paper presents an approach for tackling constrained underwater glider path planning (UGPP), where the feasible path area is defined as a corridor around the border of an ocean eddy. The objective of the glider here is to sample the oceanographic variables more efficiently while keeping a bounded trajectory. Therefore, we propose a solution based on differential evolution (DE) algorithm mechanisms, including in its configuration self-adaptation of control parameters, population size reduction, ϵ-constraint handling with adjustment, and mutation based on elitistic best vector. Different aspects of this DE configuration are studied for the constrained UGPP challenge, on a prepared benchmark set comprised of 28 different specialized scenarios. The DE configurations were tested over a benchmark set over 51 independent runs for each DE configuration aspect. Comparison and suitability for the combination of these mechanisms is reported, through the per-scenario and aggregated statistical performance differences, including different constraint handling definition strategies, different DE mutation strategies’ configurations, and population sizing parameterizations. Our proposed solution outranked all other compared algorithms, keeping a trajectory within the limits with 100% success rate in all physically feasible scenarios; on average, it improved the randomly initialized trajectories fitness by roughly 50%, even reaching perfect fitness (all-around, 360-degree eddy corridor sampling) in some scenarios.

Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.

Operation Optimization of Slab Reheating Process Based on Differential Evolution

In the slab reheating process, the temperature distribution of walking beam furnace is critical for the temperature of slab, as well as the subsequent hot rolling. Besides, this process is dynamic, nonlinear, and time varying due to coupling with complex physical and chemical reactions. In this paper, we focus on an operation optimization problem of tuning furnace temperature to optimize the slab reheating process, which considers the reheating quality and production cost. For this purpose, we develop a novel operation optimization method. Specifically, first, from the view of mechanism, a heat transmit model based on the heat-exchange of slab and furnace, an energy consumption function, and especially, an oxidation loss function are established. Therefore, incorporating the mechanism models, we build an optimization model to minimize temperature deviation of slab, energy consumption, and oxidation loss to describe our problem. Then, based on the features of the problem, we propose a modified differential evolution algorithm, which includes a space contraction scheme, a new self-adaptive parameter strategy, and a new mutation operator. Finally, to evaluate the performance of our method, extensive numerical experiments are implemented by comparing it with other well-known evolutionary algorithms based on practical data. The experimental results demonstrate the effectiveness of proposed operation optimization method on solving our problem.

A self-adaptive multi-objective harmony search algorithm based on harmony memory variance

Although harmony search (HS) algorithm has shown many advantages in solving global optimization problems, its parameters need to be set by users according to experience and problem characteristics. This causes great difficulties for novice users. In order to overcome this difficulty, a self-adaptive multi-objective harmony search (SAMOHS) algorithm based on harmony memory variance is proposed in this paper. In the SAMOHS algorithm, a modified self-adaptive bandwidth is employed, moreover, the self-adaptive parameter setting based on variation of harmony memory variance is proposed for harmony memory considering rate (HMCR) and pitch adjusting rate (PAR). To solve multi-objective optimization problems (MOPs), the proposed SAMOHS uses non-dominated sorting and truncating procedure to update harmony memory (HM). To demonstrate the effectiveness of the SAMOHS, it is tested with many benchmark problems and applied to solve a practical engineering optimization problem. The experimental results show that the SAMOHS is competitive in convergence performance and diversity performance, compared with other multi-objective evolutionary algorithms (MOEAs). In the experiment, the impact of harmony memory size (HMS) on the performance of SAMOHS is also analyzed.

A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models

Trajectory prediction of objects in moving objects databases (MODs) has garnered wide support in a variety of applications and is gradually becoming an active research area. The existing trajectory prediction algorithms focus on discovering frequent moving patterns or simulating the mobility of objects via mathematical models. While these models are useful in certain applications, they fall short in describing the position and behavior of moving objects in a network-constraint environment. Aiming to solve this problem, a hidden Markov model (HMM)-based trajectory prediction algorithm is proposed, called Hidden Markov model-based Trajectory Prediction (HMTP). By analyzing the disadvantages of HMTP, a self-adaptive parameter selection algorithm called HMTP $\ast$ is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed. In addition, a density-based trajectory partition algorithm is introduced, which helps improve the efficiency of prediction. In order to evaluate the effectiveness and efficiency of the proposed algorithms, extensive experiments were conducted, and the experimental results demonstrate that the effect of critical parameters on the prediction accuracy in the proposed paradigm, with regard to HMTP $\ast$ , can greatly improve the accuracy when compared with HMTP, when subjected to randomly changing speeds. Moreover, it has higher positioning precision than HMTP due to its capability of self-adjustment.

基于自适应改进的压缩域红外弱小目标检测

基于自适应改进的压缩域红外弱小目标检测

Image Fuzzy Enhancement Based on Self-Adaptive Bee Colony Algorithm

In the image acquisition or transmission, the image may be damaged and distorted due to various reasons; therefore, in order to satisfy people’s visual effects, these images with degrading quality must be processed to meet practical needs. Integrating artificial bee colony algorithm and fuzzy set, this paper introduces fuzzy entropy into the self-adaptive fuzzy enhancement of image so as to realize the self-adaptive parameter selection. In the meanwhile, based on the exponential properties of information increase, it proposes a new definition of fuzzy entropy and uses artificial bee colony algorithm to realize the self-adaptive contrast enhancement under the maximum entropy criterion. The experimental result shows that the method proposed in this paper can increase the dynamic range compression of the image, enhance the visual effects of the image, enhance the image details, have some color fidelity capacity and effectively overcome the deficiencies of traditional image enhancement methods.

A stereo matching in complex scene and occlusion handling model

An algorithm is proposed which the tree structure of dynamic programming with self-adaptive parameters and a disparity iterative refinement to improve stereo matching performance. The algorithm model consists of an initialization model and a disparity iterative refinement. To obtain the initial disparity map with “clear” background and whole details, the tree structure of dynamic programming with self-adaptive parameters is presented. A disparity iterative refinement is proposed to estimate the disparity of unreliable pixels in the doubtful regions. The disparity iterative refinement consists of a doubtful regions growing method and a disparity filling based on color similarity. The results of experiment evaluated with Middlebury data sets show that our algorithm can effectively ameliorate “fatting inflating” by occlusion and obtains a high quality of disparity map.

Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios

This paper proposes a novel bio-inspired neural system based on Self-organizing Maps (SOMs) and Cellular Neural Networks (CNNs), called SOM-CNN, to detect dynamic objects in normal and complex scenarios. A contribution of our work is a Retinotopic SOM (RESOM) architecture feasible for video and motion analysis. It is inspired by the visual perception mechanism of the human visual cortex, and satisfactorily addresses the disadvantages encountered by other methods in the area. We also propose a new CNN scheme for image thresholding, called Neighbor Threshold CNN (NTCNN), and a self-adapting parameter scheme for the RESOM and the NTCNN models. The proposed system can deal with sudden and gradual illumination changes, dynamic backgrounds, camouflage, camera jitter, and stopped dynamic objects. Experimental results on complex scenarios, using the Precision (Pe), Recall (Rc), F measure, (F1) and Similarity (Si) metrics, yield acceptable average performances with Pe=0.875, Rc=0.8316, F1=0.843 and Si=0.741. Results also show that our proposed system performs better than other methods that have been suggested in the literature. The system can process information at 35fps, rendering it suitable for real-time applications.

Modified differential evolution with self-adaptive parameters method

The differential evolution algorithm (DE) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problem. In this paper, the proposed algorithm uses two mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, two new rules are combined through a probability rule. Then, self-adaptive parameter setting is introduced as uniformly random numbers to enhance the diversity of the population based on the relative success number of the proposed two new parameters in a previous period. In other aspects, our algorithm has a very simple structure and thus it is easy to implement. To verify the performance of MDE, 16 benchmark functions chosen from literature are employed. The results show that the proposed MDE algorithm clearly outperforms the standard differential evolution algorithm with six different parameter settings. Compared with some evolution algorithms (ODE, OXDE, SaDE, JADE, jDE, CoDE, CLPSO, CMA-ES, GL-25, AFEP, MSAEP and ENAEP) from literature, experimental results indicate that the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained.

A New Method for Forecasting of Non-Point Source Pollutant Load

Gaussian process (GP) is a newly developed machine learning technology based on statistical theoretical fundamentals, which has successful application in the field of solving for highly nonlinear problems. Conventional methods for forecasting of non-point source pollutant load often meet great difficulty since relationship between pollutant load and its influencing factors is highly complicated nonlinear. A new method based on GP is proposed for forecasting of non-point source pollutant load. The monitoring data of a certain river since 1976 to 1990 are preformed to obtain the training samples and test samples. Nonlinear mapping relationship between non-point source pollutant load and its influencing factors can be constructed by GP learning with the training samples. The monitoring data of a certain river since 1991 to 1993 are preformed to testify the effects of the method above. The results of case studies show that the method is feasible, effective and simple to implement for forecasting of non-point source pollutant load. It has merits of self-adaptive parameters determination and better capacity for solving nonlinear small sample problems comparing with the artificial neural networks method and Support Vector Machine method. The good performance of GP model makes it very attractive for a wide range of application in environmental engineering.

An adaptive invasion-based model for distributed Differential Evolution

A novel adaptive model for a recently devised distributed Differential Evolution algorithm is introduced. The distributed algorithm, following the stepping-stone model, is characterized by a migration model inspired by the phenomenon known as biological invasion. The adaptive model is endowed with three updating schemes to randomly set the mutation and the crossover parameters. These schemes are here tied to the migration and are guided by a performance measure between two consecutive migrations. The proposed adaptive model is tested on a set of classical benchmark functions over the different setting schemes. To evaluate its performance, the model is compared against the original non-adaptive version with a fixed parameter setting, and against a well-known distributed Differential Evolution algorithm equipped with the same schemes for the control parameter updating. The experimental study shows that the method results in high effectiveness in terms of solutions detected and convergence speed on most of the benchmark problems and for the majority of the setting schemes investigated. Finally, to further estimate its effectiveness, the proposed approach is also compared with several state-of-the-art Differential Evolution frameworks endowed with different randomized or self-adaptive parameter setting strategies. This comparison shows that our adaptive model allows obtaining the best performance in most of the tests studied.

On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms

We consider search-based procedural content generation in the context of Planet Wars, an RTS game. The objective of this work is to generate maps for the aforementioned game, that result in an interesting game-play. In order to characterize interestingness we focus on the properties of balance and dynamism. The former captures the fact that no player is overwhelmed by the opponent during the game, whereas the latter tries to model the fact that there is a lot of action during the game. To measure these properties on a given map, we conduct several games on them using top AI bots and collect statistics which are, in turn, used as inputs of a fuzzy rule base. This system is embedded within an evolutionary algorithm that features self-adaptation of mutation parameters as well as variable-length chromosomes (thus implying maps of different sizes). The experimentation focuses both on the optimization of balance and dynamism as stand-alone properties and in the analysis of the different tradeoffs attainable through them. To reach this goal a multi objective approach is used. We analyze both the usefulness of map-size self-adaptation in each scenario, as well as the properties of maps leading to different tradeoffs between dynamism and balance.