Kind Of Evolutionary Algorithm Research Articles

Robust estimation of distribution algorithms via fitness landscape analysis for optimal low-thrust orbital maneuvers

One particular kind of evolutionary algorithms known as Estimation of Distribution Algorithms (EDAs) has gained the attention of the aerospace industry for its ability to solve nonlinear and complicated problems, particularly in the optimization of space trajectories during on-orbit operations of satellites. This article describes an effective method for optimizing the trajectory of a spacecraft using an evolutionary approach based on EDAs, incorporated with fitness landscape analysis (FLA). The approach utilizes flexible operators that are paired with seeding and selection mechanisms of EDAs. Initially, the orbit transfer problem is mathematically modeled and the objectives and constraints are identified. The landscape feature of the search space is analyzed via the dispersion metric to measure the modality and ruggedness of the search domain. The obtained information are used as feedback in developing adaptive operators for truncation factor and constraints separation threshold of the employed EDA. A framework for spacecraft trajectory optimization has been presented where the dispersion value for a space mission is estimated using a k-nearest neighbors (k-NN) algorithm. The suggested method is used to solve several problems related to low-thrust orbit transfer of satellites in Earth’s orbit. Results demonstrate that the suggested framework for trajectory design and optimization of space transfers is effective enough to offer fuel-efficient and energy-efficient maneuvers for different thrust levels of the propulsion system. Moreover, the performance of the proposed approach is evaluated against non-adaptive EDA and other advanced evolutionary algorithms. The obtained results certify that the proposed adaptive evolutionary approach is superior in identifying feasible minimum-fuel and minimum-energy transfer trajectories.

Optimization of flow shop scheduling based on genetic algorithm with reinforcement learning

Abstract Genetic algorithm, as a kind of evolutionary algorithm, has the characteristics of easy operation and global search, but its stochasticity is relatively strong and highly susceptible to parameters. When facing a large-scale scheduling problem, a strategy is needed to improve the parameter adaptability to make its solution more effective. Reinforcement learning, as an optimization method, has a strong autonomous learning capability. Therefore, this paper proposes a genetic algorithm based on reinforcement learning, which uses Q-learning to self-learning the crossover probability and improve the generalization ability of genetic algorithm, so as to achieve the solution of large-scale replacement flow shop scheduling problem.

Evolutionary Multitask Optimization With Adaptive Knowledge Transfer

Evolutionary multitask optimization (EMTO) studies how to simultaneously solve multiple optimization tasks via evolutionary algorithms (EAs) while making the useful knowledge acquired from solving one task to assist solving other tasks, aiming to improve the overall performance of solving each individual task. Recent years have seen a large body of EMTO works based on different kinds of EAs and studying one or more aspects in how to represent, extract, transfer, and reuse knowledge. A key challenge to EMTO is the occurrence of negative knowledge transfer between tasks, which becomes severer when the total number of tasks increases. To address this issue, we propose an adaptive EMTO (AEMTO) framework. This framework can adapt knowledge transfer frequency, knowledge source selection, and knowledge transfer intensity in a synergistic way to make the best use of knowledge transfer, especially when facing many tasks. We implement the proposed AEMTO framework and evaluate our implementation on three suites of MTO problems with 2, 10, and 50 tasks and one real-world MTO problem with 2000 tasks in comparison to several state-of-the-art EMTO methods with certain adaptation strategies regarding knowledge transfer and the single-task optimization counterpart of the proposed method. Experimental results have demonstrated the effectiveness of the adaptive knowledge transfer strategies used in AEMTO and the overall performance superiority of AEMTO.

A Hybrid Forecasting Model for the Velocity of Hybrid Robotic Fish Based on Back-Propagation Neural Network With Genetic Algorithm Optimization

Marine unmanned vehicle is a novel robot widely used in ocean observation, and its accurate control is of significance to their path planning. We want to find a method to predict the velocity and course of this robot, which can help us realize the accurate control of it. The paper proposed a promising type of hybrid robotic fish (HRF), which can realize two kinds of motion modes on the sea surface. Firstly, the configuration and dynamic model of the HRF were analyzed elaborately. Then, to realize accurate velocity prediction under two kinds of motion modes of HRF, the influence factors are presented in a complex marine environment. Based on the influence factors to its maneuverability, such as wind or wave parameters, a velocity prediction algorithm based on back-propagation neural network (BPNN) was introduced. However, BPNN has the disadvantages of extended learning and training time, easily falling into local optimum. Then we found that genetic algorithm (GA), which is a kind of evolutionary algorithm, is suitable for our problem. Therefore, the accuracy and efficiency of the prediction algorithm were improved by adopting the genetic algorithm to optimize the weight and threshold of BPNN. Taking the experimental data from the pool test, the back-propagation neural network with genetic algorithm (GA-BPNN) forecasting model was established. Besides, the other prediction methods were compared and evaluated under the same assessment criterion to validate the proposed forecasting model. The experimental results demonstrate that the GA-BPNN model has higher accuracy and efficiency compared with other prediction algorithms, which verifies the feasibility of the velocity prediction model for hybrid robotic fish in complex ocean environments.

Application of the asynchronous advantage actor–critic machine learning algorithm to real-time accelerator tuning

This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic (A3C) algorithm for accelerator systems. The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation. Therefore, parameter optimization methods such as pointwise scanning, evolutionary algorithms (EAs), and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency. However, it is difficult for them to deal with a large number of discrete local optima. The A3C algorithm, which has been applied in the automated control field, provides an approach for improving multi-dimensional optimization. The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators. Experiments in which optimization is achieved by using pointwise scanning, the genetic algorithm (one kind of EAs), and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section (LEBT) of the Xi’an Proton Application Facility. Optimal currents are determined when the highest transmission of a radio frequency quadrupole (RFQ) accelerator downstream of the LEBT is achieved. The optimal work points of the tuned accelerator were obtained with currents of 0 A, 0 A, 0 A, and 0.1 A, for the four steering magnets, and 107 A and 96 A for the two solenoids. Furthermore, the highest transmission of the RFQ was 91.2%. Meanwhile, the lower time required for the optimization with the A3C algorithm was successfully verified. Optimization with the A3C algorithm consumed 42% and 78% less time than pointwise scanning with random initialization and pre-trained initialization of weights, respectively.

On the selection of solutions for mutation in differential evolution

Differential evolution (DE) is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.

An enhanced artificial neural network with a shuffled complex evolutionary global optimization with principal component analysis

The classical Back-Propagation (BP) scheme with gradient-based optimization in training Artificial Neural Networks (ANNs) suffers from many drawbacks, such as the premature convergence, and the tendency of being trapped in local optimums. Therefore, as an alternative for the BP and gradient-based optimization schemes, various Evolutionary Algorithms (EAs), i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), and Differential Evolution (DE), have gained popularity in the field of ANN weight training. This study applied a new efficient and effective Shuffled Complex Evolutionary Global Optimization Algorithm with Principal Component Analysis – University of California Irvine (SP-UCI) to the weight training process of a three-layer feed-forward ANN. A large-scale numerical comparison is conducted among the SP-UCI-, PSO-, GA-, SA-, and DE-based ANNs on 17 benchmark, complex, and real-world datasets. Results show that SP-UCI-based ANN outperforms other EA-based ANNs in the context of convergence and generalization. Results suggest that the SP-UCI algorithm possesses good potential in support of the weight training of ANN in real-word problems. In addition, the suitability of different kinds of EAs on training ANN is discussed. The large-scale comparison experiments conducted in this paper are fundamental references for selecting proper ANN weight training algorithms in practice.

A genetic algorithm to solve 3D traveling salesman problem with initial population based on a GRASP algorithm

In this paper, the problem of obtaining optimal routes on tridimensional environments is discussed. This scenario is called as Traveler Salesman Problem (TSP 3D-variation). As is widely known, TSP has NP-complexity so is necessary to apply techniques to solve it approximately (no exacts solutions a vailable). The purpose of this research is to present a genetic algorithm to solve 3D-TSP variation. These kind of evolutionary algorithms are ideal for solving complex problems where necessary rearrangements and route optimization. In case of genetic algorithms, optimal solutions appear faster depending on the quality of initial population, so theory recommends using metaheuristics for generating this population. In this study, it has used a metaheuristic GRASP algorithm to generate the initial population and, over it, apply the genetic operators proposed for optimizing individuals obtained. The results have optimal routes of movement and displacement and are directly applicable in the storage industry.

Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images

Nowadays, Melanoma has become one of the most significant public health concerns. Malignant Melanoma (MM) is considered the most rapidly spreading type of skin cancer. In this paper, we have built models for detection, segmentation, and classification of Melanoma in skin images using evolutionary algorithms. The first step was to enhance the K-mean algorithm by using two kinds of Evolutionary Algorithms: a Genetic Algorithm and the Particle Swarm Algorithm. Then the Enhanced Algorithms and the default k-mean separately were used to do detection and segmentation of skin cancer images. Then a feature extraction step was applied on the segmented images. Finally, the classification step was done by using two predictive models. The first model was built using a Neural Network backpropagation and the other one using some threshold values for some selected features. The results showed a high accuracy using Neural Back-propagation for the Enhanced K-mean by using a Genetic Algorithm, which achieved 87.5%.

Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions

Novel Evolutionary Algorithms are usually tested on sets of artificially-constructed benchmark problems. Such problems are often created to make the search of one global extremum (usually minimum) tricky. In this paper it is shown that benchmarking heuristics on either minimization or maximization of the same set of artificially-created functions (with equal bounds and number of allowed function calls) may lead to very different ranking of tested algorithms. As Evolutionary Algorithms and other heuristic optimizers are developed in order to be applicable to real-world problems, such result may raise doubts on the practical meaning of benchmarking them on artificial functions, as there is little reason that searching for the minimum of such functions should be more important than searching for their maximum.Thirty optimization heuristics, including a number of variants of Differential Evolution, as well as other kinds of Evolutionary Algorithms, Particle Swarm Optimization, Direct Search methods and – following the idea borrowed from No Free Lunch – pure random search are tested in the paper. Some discussion regarding the choice of the mean or the median performance for comparison is addressed and a short debate on the overall performance of particular methods is given.

Integration Particle Swarm Algorithm and its Application in Neural Network

This paper puts forward a kind of evolutionary algorithm and the neural network combining with the new method of optimization of hidden layer nodes number of particle swarm algorithm of neural network. The BP neural network technology is a kind of more mature neural network method, but there are easy to fall into local minimum value, unable to accurately determine the number of hidden layer nodes of the network, the disadvantages such as slow convergence speed. This paper puts forward the optimization with hidden node number of particle swarm neural network (HPSO neural network) is the hidden layer of BP network node number as a particle swarm optimization (PSO) algorithm is an important optimization goal, network of hidden layer nodes and the number of each BP network weights and closed value together, common as particle swarm algorithm optimization goal.

Chaotic biogeography-based optimisation

Evolutionary algorithms (EAs) performs much better than traditional ones in solving optimisation problems. Biogeography-based optimisation (BBO) is a newly proposed kind of EAs, robust and needs little extra information when doing optimisation. Chaos is actually a mapping that exhibits some sort of chaotic behaviour, and has the features of randomness and ergodicity, which make chaos optimisation more elaborate in a certain domain without repeated searching. In this paper, BBO and chaos are merged together and chaotic biogeography-based optimisation method is proposed for the first time. According to several famous chaotic models, corresponding chaotic biogeography-based optimisation methods are produced. Comparison of these methods with other algorithms from numerical test shows that the new hybrid optimisation method doses a better job on many benchmark functions.

An improved AEA algorithm with Harmony Search(HSAEA) and its application in reaction kinetic parameter estimation

Alopex-based evolutionary algorithm (AEA) is one kind of evolutionary algorithm. It possesses the basic characteristics of evolutionary algorithms as well as the advantages of gradient descent methods and simulation anneal algorithm, but it is also easy to trap into a local optimum. For the AEA algorithm, the unreasonably settings of compared population and step length are two typical drawbacks, which lead to the lack of communication between individuals in each generation. In this paper, estimation of distribution algorithm (EDA) is employed to generate the compared population. Then the moving step length in AEA is improved to vary with different phases of the iteration during the actual operation process. And more importantly, harmony search algorithm (HS) is introduced to improve the quality of population of every generation. By compared with original AEA, the performance of the improved algorithm (HSAEA) was tested on 22 unconstrained benchmark functions. The testing results show that HSAEA clearly outperforms original AEA for almost all the benchmark functions. Furthermore, HSAEA is used to estimate reaction kinetic parameters for a heavy oil thermal cracking three lumps model and Homogeneous mercury (Hg) oxidation, and satisfactory results are obtained.

Playing with complexity: From cellular evolutionary algorithms with coalitions to self-organizing maps

Since its origins, Cellular Automata (CA) has been used to model many type of physical and computational phenomena. Interacting CAs in spatial lattices combined with evolutionary game theory have been very popular for modeling genetics or behavior in biological systems. Cellular Evolutionary Algorithms (cEAs) are a kind of evolutionary algorithm (EA) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in EAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. A new adaptive technique (EACO) based on Cellular Automata, Game Theory and Coalitions uses dynamic neighborhoods to enhance the quality of cEAs. In this article we compare the characteristics EACO with classical Self-organizing Maps (SOM), and we discuss the possibilities for using Game Theory and Coalitions in the SOM scenario.

A differential evolution algorithm with cooperative coevolutionary selection operation for high-dimensional optimization

Although different kinds of evolutionary algorithms (EAs) have been designed and achieved great success on many optimization problems, they are usually limited to some small-scale problems, e.g. with less than 100 decision variables, which may be quite small comparing to the requirements of real-world applications. Therefore, scaling EAs to large size problems have attracted more and more interest. Conventional EAs mimic the seemingly random natural processes by which species evolve. These evolution processes are slow or inefficient. Now, genetic engineering has enabled man to increase both the yields and quality of some crops fast by modifying some part of their genome precisely. In this paper, inspired by the ideas of the genetic engineering, we designed a local selection operator by decomposing the high-dimensional problem into some subcomponents and assigning a local fitness function to evaluate each subcomponent. Then a new differential evolution (DE) is proposed by inserting the local selection operator into the framework of DE. Numerical experiments were carried out to evaluate the performance of the new algorithm on a large number of benchmark functions. The results show that the new algorithm is effective and efficient for high-dimensional optimization.

Implementation of Artificial Bee Colony Algorithm

Evolutionary algorithm is a stochastic search method that mimics the natural biological evolution and the social behavior of species. Artificial bee colony algorithm is also a kind of evolutionary algorithm which was proposed by Dervis karaboga in 2005.Such algorithms have been developed to arrive at near-optimum solutions of multimodal optimization problems, which may not be possible with traditional algorithms. This paper describes implementation of ABC algorithm on complex benchmark functions like rastrigin, rosenbrock; sphere and schwefel the analysis of the performance of ABC algorithm were compared for the optimization of above benchmark functions with Partical Swarm Optimization (PSO). The ABC algorithm was successfully implemented in software tool ‘c’. DOI: http://dx.doi.org/10.11591/ij-ai.v1i3.588

Research of Space Electronic Circuit Optimization Design

During the space electronic system in carries out the exploratory mission in the deep space, it maybe faced with kinds of violent natural environment, to electric circuit's performance, the volume, the weight and the stability proposed a higher request, the traditional circuit design method already more and more with difficulty satisfied this kind of request. The traditional circuit design method already more and more with difficulty satisfied this kind of request. But unifies the programmable component and the evolutionary algorithms hardware may the dynamic change hardware's structure adapt the adverse circumstance, resume the damage of the function, the adaptation for the duty change. In view of the Xilinx Company's FPGA unique feature, proposed one kind of evolutionary algorithms which uses in the space electronic system circuit optimization design and through the experiment proved, the algorithm obtains the circuit structure to surpass the tradition circuit design method.

Circuit Optimization Design Using Evolutionary Algorithms

During the space electronic system in carries out the exploratory mission in the deep space, it maybe faced with kinds of violent natural environment, to electric circuit's performance, the volume, the weight and the stability proposed a higher request, the traditional circuit design method already more and more with difficulty satisfied this kind of request. The traditional circuit design method already more and more with difficulty satisfied this kind of request. But unifies the programmable component and the evolutionary algorithms hardware may the dynamic change hardware's structure adapt the adverse circumstance, resume the damage of the function, the adaptation for the duty change. After the optimization, obtains the circuit structure will often stem from our anticipation, this will be the altitude which the experience and the skillful institute hope to attain with difficulty. In view of the Xilinx Company's FPGA unique feature, proposed one kind of evolutionary algorithms which uses in the space electronic system circuit optimization design and through the experiment proved, the algorithm obtains the circuit structure to surpass the tradition circuit design method. This work investigates the application of genetic algorithms in the field of circuit optimization. For the case studies, this means has proved to be efficient and the experiment results show that the new means have got the better results.

Estimation of Distribution Algorithms as Logistic Regression Regularizers of Microarray Classifiers

The "large k (genes), small N (samples)" phenomenon complicates the problem of microarray classification with logistic regression. The indeterminacy of the maximum likelihood solutions, multicollinearity of predictor variables and data over-fitting cause unstable parameter estimates. Moreover, computational problems arise due to the large number of predictor (genes) variables. Regularized logistic regression excels as a solution. However, the difficulties found here involve an objective function hard to be optimized from a mathematical viewpoint and a careful required tuning of the regularization parameters. Those difficulties are tackled by introducing a new way of regularizing the logistic regression. Estimation of distribution algorithms (EDAs), a kind of evolutionary algorithms, emerge as natural regularizers. Obtaining the regularized estimates of the logistic classifier amounts to maximizing the likelihood function via our EDA, without having to be penalized. Likelihood penalties add a number of difficulties to the resulting optimization problems, which vanish in our case. Simulation of new estimates during the evolutionary process of EDAs is performed in such a way that guarantees their shrinkage while maintaining their probabilistic dependence relationships learnt. The EDA process is embedded in an adapted recursive feature elimination procedure, thereby providing the genes that are best markers for the classification. The consistency with the literature and excellent classification performance achieved with our algorithm are illustrated on four microarray data sets: Breast , Colon , Leukemia and Prostate . Details on the last two data sets are available as supplementary material. We have introduced a novel EDA-based logistic regression regularizer. It implicitly shrinks the coefficients during EDA evolution process while optimizing the usual likelihood function. The approach is combined with a gene subset selection procedure and automatically tunes the required parameters. Empirical results on microarray data sets provide sparse models with confirmed genes and performing better in classification than other competing regularized methods.

Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization

Randomized search heuristics like local search, tabu search, simulated annealing, or all kinds of evolutionary algorithms have many applications. However, for most problems the best worst-case expected run times are achieved by more problem-specific algorithms. This raises the question about the limits of general randomized search heuristics. Here a framework called black-box optimization is developed. The essential issue is that the problem but not the problem instance is knownto the algorithm which can collect information about the instance only by asking for the value of points in the search space. All known randomized search heuristics fit into this scenario. Lower bounds on the black-box complexity of problems are derived without complexity theoretical assumptions and are compared with upper bounds in this scenario.