A nonlinear African vulture optimization algorithm combining Henon chaotic mapping theory and reverse learning competition strategy

The swarm intelligence algorithm is a new technology proposed by researchers inspired by the biological behavior of nature, which has been practically applied in various fields. As a kind of swarm intelligence algorithm, the newly proposed sparrow search algorithm has attracted extensive attention due to its strong optimization ability. Aiming at the problem that it is easy to fall into local optimum, this paper proposes an improved sparrow search algorithm (IHSSA) that combines infinitely folded iterative chaotic mapping (ICMIC) and hybrid reverse learning strategy. In the population initialization stage, the improved ICMIC strategy is combined to increase the distribution breadth of the population and improve the quality of the initial solution. In the finder update stage, a reverse learning strategy based on the lens imaging principle is utilized to update the group of discoverers with high fitness, while the generalized reverse learning strategy is used to update the current global worst solution in the joiner update stage. To balance exploration and exploitation capabilities, crossover strategy is joined to update scout positions. 14 common test functions are selected for experiments, and the Wilcoxon rank sum test method is achieved to verify the effect of the algorithm, which proves that IHSSA has higher accuracy and better convergence performance to obtain solutions than 9 algorithms such as WOA, GWO, PSO, TLBO, and SSA variants. Finally, the IHSSA algorithm is applied to three constrained engineering optimization problems, and satisfactory results are held, which proves the effectiveness and feasibility of the improved algorithm.

Clinical pathways are management plans that display goals for patients and the sequence and timing of actions necessary to achieve those goals with optimal efficiency. Given the resource competition between multiple clinical pathways, clinical pathway scheduling is an urgent and meaningful question to be solved, which is a typical resource constrained multi-project scheduling problem (RCMPSP). Intelligent optimization algorithms are widely used to solve RCMPSP problems. In this paper, considering the shortcomings of the existing optimal scheduling algorithms, which is insufficient in search and easy to fall into local optimum, we construct a model for clinical pathway scheduling and propose a hybrid intelligent optimization algorithm. We also design the encoding and decoding rules and fitness function of the optimization. Experiment results on renal calculi clinical pathway show that the proposed algorithm has a better search capabilities in clinical pathway scheduling and preforms better performance when the number of patients increases.

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA’s unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

In recent years, with the great success of dendritic neuron model (DNM) in various fields, the application of intelligent optimization algorithms in dendritic neuron model has attracted increasing attention of researchers. The training process of neural network is regarded as one of the great challenges of machine learning because of its non-linear nature and unknown optimal parameters. The traditional training algorithm of DNM is prone to fall into local optimum and speed of convergence slowly and so on, resulting in the problem of accuracy and low efficiency. In this paper, for solving the classification problem of dendritic neural model, an innovative intelligent optimization algorithm which named whale optimization algorithm (WOA), is applied to the training of DNM for the first time. Compared with six traditional and classic intelligent optimization algorithms in four classic datasets, the results indicate that WOA-DNM has good performance in various aspects, and its advantage is remarkable.

Two approaches are proposed for short-term forecast of the parameters of expected operating conditions. The Kalman filter based algorithms and the modern technologies of an artificial intelligence and nonlinear optimization algorithms are employed for dynamical state estimation. The new approach combining the artificial neural networks and the Hilbert-Huang transform is designed in order to increase the accuracy of operating conditions forecasting. Numerical experiments on real time series have demonstrated the improvement of the prediction.

The co-occurrence matrix is a way to describe the spatially relevant information of an image. The image thresholding method based on the co-occurrence matrix has its advantages over the thresholding method based on 2D grey-scale histogram. The traditional 2D grey-scale histogram requires two thresholds for binary segmentation, whereas the grey-scale co-occurrence matrix requires only one threshold for binary segmentation. When performing multi-threshold segmentation of an image, using co-occurrence matrix reduces the number of thresholds by half compared to using two-dimensional grey-scale histogram. In this paper, exponential entropy is used to implement co-occurrence matrix threshold selection, and swarm intelligent optimization algorithms is used to reduce the computational effort of multi-threshold selection. This method is compared with the 2D histogram-based method and the traditional log-entropy-based threshold segmentation method on BSD500 images, and the metrics of PSNR, FSIM, and SSIM are analysed in conjunction with the three intelligent optimization algorithms, and the methods in this paper are all superior to other comparative methods.

Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems, it easily falls into local optima when solving complex multimodal problems. This paper aims to enhance the performance of PSO in complex optimization problems and proposes an improved PSO variant by incorporating a novel mutation operator. Experimental studies on some well-known benchmark problems show that our approach achieves promising results.

Value function approximation, such as Q-learning, is widely used in the discrete control rather than the continuous one because the optimal action in the discrete setting is more easily selected. Optimizing the action is a non-convex optimization problem with respect to the complex value function. Some notable studies simplify the non-convex optimization problem by assuming the value function as quadratic in the actions or by discretizing the action space. However, the performance of the output policy will decline if these studies’ premises do not hold. In order to address the problem, we propose a framework that combines swarm intelligence algorithms with value-based Reinforcement Learning, where the swarm intelligence algorithms are employed to search for the optimal action with respect to the state and the value function. To ensure the correctness of this framework, we conditionally claim the convergence rate of swarm intelligence algorithms with high probability. We then implement it by searching the batch optimal actions to various states on the GPU platform for the batch training. Furthermore, we employ the population-based atomic actions for the compatibility with the existing related work about solving discrete control problems. Four classical control models and four robot simulation environments are utilized in the comparisons. According to empirical results, our framework outputs a policy comparable with that of the policy-based algorithms by 10% timesteps in the continuous control. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the exploration-exploitation dilemma of Reinforcement Learning to solve continuous control tasks. To balance the exploration and exploitation, the stochastic exploration and the prioritized exploration are roughly two feasible ways, where the prioritized one is a better choice due to the higher data efficiency than the stochastic one, e.g. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon$</tex-math> </inline-formula> -greedy. Normally, the prioritized exploration works well in the value-based Reinforcement Learning algorithms rather than the policy-based ones; meanwhile, the policy-based algorithms are more suitable to continuous control tasks than the value-based ones. To tackle this conflict, we especially design a particle swarm optimization to maximize the Q-value of action in Q-learning. Our design can be hybridized by various swarm intelligence and value-based Reinforcement Learning algorithms. Also, it can be embedded in most intelligent control systems easily. The aim of this study is to solve the continuous control tasks by value-based algorithms as the first step of applying the prioritized exploration. The simulative results verify the effectiveness and efficiency of our design.

In view of the fact that the classical fast motion estimation methods are easy to fall into local optimum and suffer the high computational cost, the convergence of the motion estimation method based on the swarm intelligence algorithm is very slow. A new block motion estimation method based on single-dimension perturbation glowworm swarm optimization algorithm is proposed. Single-dimension perturbation is a local search strategy which can improve the ability of local optimization. The proposed method not only has overcome the defect of falling into local optimum easily by taking use of both the global search ability of glowworm swarm optimization algorithm and the local optimization ability of single-dimension perturbation but also has reduced the computation complexity by using motion vector predictor and terminating strategies in view of the characteristic of video images. The experimental results show that the performance of the proposed method is better than that of other motion estimation methods for most video sequences, specifically for those video sequences with violent motion, and the searching precision has been improved obviously. Although the computational complexity of the proposed method is slightly higher than that of the classical methods, it is still far lower than that of full search method.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

Based on the requirements of a certain embedded software evaluation task in China’s space station, this paper proposes a MC/DC test case generation method based on improved drosophila optimization algorithm. Compared with other intelligent optimization algorithms, drosophila algorithm has the advantages of small computation, low complexity and few debugging parameters. MC/DC coverage is one of the logic coverage methods in white box test, it is widely used in defense and aerospace. In this paper, based on the MC/DC target path method, the iterative optimization ability of drosophila algorithm is used to find the test cases that meet the target path. At the same time, due to the instability of the drosophila algorithm and the tendency to fall into local optimum, the rate of change of the global optimal value is included in the weighting process in the iterative process to improve the drosophila optimization algorithm. Finally, we select a typical verification program to generate MC/DC test cases through simulation experiments. The effectiveness of the proposed method is verified, which provides a new method for coverage test in the space station software evaluation task.

Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems

PSO may easily get trapped in a local optimum, when solving complex multimodal problems. In this paper, an improved PSO based on small world network and comprehensive is proposed. The learning exemplar of each particle includes three parts: the global best particle, its own best particle (pbest) and the pbest of its neighborhood. And a random position around itself is needed to increase a probability to jump to that promising region. These strategies enable the diversity of the swarm to be preserved to discourage premature convergence. In benchmark function test, the SCPSO algorithm achieves better solutions than other PSOs.

Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithmic equivalence of particle swarm optimization (PSO) and various other newer SI algorithms, including the shuffled frog leaping algorithm (SFLA), the group search optimizer (GSO), the firefly algorithm (FA), artificial bee colony algorithm (ABC) and the gravitational search algorithm (GSA). We find that the original versions of SFLA, GSO, FA, ABC, and GSA, are all algorithmically identical to PSO under certain conditions. We discuss their diverse biological motivations and algorithmic details as typically implemented, and show how their differences enhance the diversity of SI research and application. Then we numerically compare SFLA, GSO, FA, ABC, and GSA, with basic and advanced versions on some continuous benchmark functions and combinatorial knapsack problems. Empirical results show that an advanced version of ABC performs best on the continuous benchmark functions, and advanced versions of SFLA and GSA perform best on the combinatorial knapsack problems. We conclude that although these SI algorithms are conceptually equivalent, their implementation details result in notably different performance levels.

The research on big data and intelligent optimization algorithm is to propose a new method to solve problems by human brain through artificial intelligence after human brain analyzes and processes it. It transforms people from complex and unstructured information into simple and intuitive mathematical models. Big data technology can help people solve some problems. Firstly, the related concepts of music teaching courses are analyzed, then the application principle and formula of intelligent optimization algorithm are studied, and a teaching quality evaluation system based on intelligent optimization algorithm is designed. At the same time, the performance of the system is tested by simulation experiments. The test results show that the system can operate normally, achieve the expected goal, and finally meet the user's evaluation requirements for the quality of music art teaching, so as to improve students' music level, comprehensive quality growth and social harmony and stability.