Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach

International audienceThis paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV) path planning with particle swarm optimization (PSO) technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples -test is made and an index called straight line rate (SLR) is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Particle swarm optimization algorithms with selective differential evolution for AUV path planning

Particle swarm optimization (PSO)-based algorithms are suitable for path planning of the Autonomous Underwater Vehicle (AUV) due to their high computational efficiency. However, such algorithms may produce sub-optimal paths or require higher computational load to produce an optimal path. This paper proposed a new approach that improves the ability of PSO-based algorithms to search for the optimal path while maintaining a low computational requirement. By hybridizing with differential evolution (DE), the proposed algorithms carry out the DE operator selectively to improve the search ability. The algorithms were applied in an offline AUV path planner to generate a near-optimal path that safely guides the AUV through an environment with a priori known obstacles and time-invariant non-uniform currents. The algorithm performances were benchmarked against other algorithms in an offline path planner because if the proposed algorithms can provide better computational efficiency to demonstrate the minimum capability of a path planner, then they will outperform the tested algorithms in a realistic scenario. Through Monte Carlo simulations and Kruskal-Wallis test, SDEAPSO (selective DE-hybridized PSO with adaptive factor) and SDEQPSO (selective DE-hybridized Quantum-behaved PSO) were found to be capable of generating feasible AUV path with higher efficiency than other algorithms tested, as indicated by their lower computational requirement and excellent path quality

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

A voting-based machine learning approach for classifying biological and clinical datasets.

BACKGROUND: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. RESULTS: The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value \u3c 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. CONCLUSION: Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC