Metaheuristic Algorithms for Convolution Neural Network

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).Comment: Article ID 1537325, 13 pages. Received 29 January 2016; Revised 15 April 2016; Accepted 10 May 2016. Academic Editor: Martin Hagan. in Hindawi Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016

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

DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization

Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework that leverages deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization method, DeepACO performs better than or on par with problem-specific methods on canonical routing problems. Our code is publicly available at https://github.com/henry-yeh/DeepACO.Comment: Accepted at NeurIPS 202

Investigating Evaluation Measures in Ant Colony Algorithms for Learning Decision Tree Classifiers

Ant-Tree-Miner is a decision tree induction algorithm that is based on the Ant Colony Optimization (ACO) meta- heuristic. Ant-Tree-Miner-M is a recently introduced extension of Ant-Tree-Miner that learns multi-tree classification models. A multi-tree model consists of multiple decision trees, one for each class value, where each class-based decision tree is responsible for discriminating between its class value and all other values present in the class domain (one vs. all). In this paper, we investigate the use of 10 different classification quality evaluation measures in Ant-Tree-Miner-M, which are used for both candidate model evaluation and model pruning. Our experimental results, using 40 popular benchmark datasets, identify several quality functions that substantially improve on the simple Accuracy quality function that was previously used in Ant-Tree-Miner-M

Nature-Inspired Topology Optimization of Recurrent Neural Networks

Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process. To address this challenge, this work presents three nature-inspired (NI) algorithms for neural architecture search (NAS), introducing the subfield of nature-inspired neural architecture search (NI-NAS). These algorithms, based on ant colony optimization (ACO), progress from memory cell structure optimization, to bounded discrete-space architecture optimization, and finally to unbounded continuous-space architecture optimization. These methods were applied to real-world data sets representing challenging engineering problems, such as data from a coal-fired power plant, wind-turbine power generators, and aircraft flight data recorder (FDR) data. Initial work utilized ACO to select optimal connections inside recurrent long short-term memory (LSTM) cell structures. Viewing each LSTM cell as a graph, ants would choose potential input and output connections based on the pheromones previously laid down over those connections as done in a standard ACO search. However, this approach did not optimize the overall network of the RNN, particularly its synaptic parameters. I addressed this issue by introducing the Ant-based Neural Topology Search (ANTS) algorithm to directly optimize the entire RNN topology. ANTS utilizes a discrete-space superstructure representing a completely connected RNN where each node is connected to every other node, forming an extremely dense mesh of edges and recurrent edges. ANTS can select from a library of modern RNN memory cells. ACO agents (ants), in this thesis, build RNNs from the superstructure determined by pheromones laid out on the superstructure\u27s connections. Backpropagation is then used to train the generated RNNs in an asynchronous parallel computing design to accelerate the optimization process. The pheromone update depends on the evaluation of the tested RNN against a population of best performing RNNs. Several variations of the core algorithm was investigated to test several designed heuristics for ANTS and evaluate their efficacy in the formation of sparser synaptic connectivity patterns. This was done primarily by formulating different functions that drive the underlying pheromone simulation process as well as by introducing ant agents with 3 specialized roles (inspired by real-world ants) to construct the RNN structure. This characterization of the agents enables ants to focus on specific structure building roles. ``Communal intelligence\u27\u27 was also incorporated, where the best set of weights was across locally-trained RNN candidates for weight initialization, reducing the number of backpropagation epochs required to train each candidate RNN and speeding up the overall search process. However, the growth of the superstructure increased by an order of magnitude, as more input and deeper structures are utilized, proving to be one limitation of the proposed procedure. The limitation of ANTS motivated the development of the continuous ANTS algorithm (CANTS), which works with a continuous search space for any fixed network topology. In this process, ants moving within a (temporally-arranged) set of continuous/real-valued planes based on proximity and density of pheromone placements. The motion of the ants over these continuous planes, in a sense, more closely mimicks how actual ants move in the real world. Ants traverse a 3-dimensional space from the inputs to the outputs and across time lags. This continuous search space frees the ant agents from the limitations imposed by ANTS\u27 discrete massively connected superstructure, making the structural options unbounded when mapping the movements of ants through the 3D continuous space to a neural architecture graph. In addition, CANTS has fewer hyperparameters to tune than ANTS, which had five potential heuristic components that each had their own unique set of hyperparameters, as well as requiring the user to define the maximum recurrent depth, number of layers and nodes within each layer. CANTS only requires specifying the number ants and their pheromone sensing radius. The three applied strategies yielded three important successes. Applying ACO on optimizing LSTMs yielded a 1.34\% performance enhancement and more than 55% sparser structures (which is useful for speeding up inference). ANTS outperformed the NAS benchmark, NEAT, and the NAS state-of-the-art algorithm, EXAMM. CANTS showed competitive results to EXAMM and competed with ANTS while offering sparser structures, offering a promising path forward for optimizing (temporal) neural models with nature-inspired metaheuristics based the metaphor of ants

Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding

Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of MAPF in complex and crowded environments, however, critically diminishes the effectiveness of existing solutions. In contrast to other studies that have either presented a general overview of the recent advancements in MAPF or extensively reviewed Deep Reinforcement Learning (DRL) within multi-agent system settings independently, our work presented in this review paper focuses on highlighting the integration of DRL-based approaches in MAPF. Moreover, we aim to bridge the current gap in evaluating MAPF solutions by addressing the lack of unified evaluation metrics and providing comprehensive clarification on these metrics. Finally, our paper discusses the potential of model-based DRL as a promising future direction and provides its required foundational understanding to address current challenges in MAPF. Our objective is to assist readers in gaining insight into the current research direction, providing unified metrics for comparing different MAPF algorithms and expanding their knowledge of model-based DRL to address the existing challenges in MAPF.Comment: 36 pages, 10 figures, published in Artif Intell Rev 57, 41 (2024

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs

Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches $\mathbf{O}(10^6)$ servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in $\approx 500 ps$ and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where $3\times$ less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in $\mathbf{O}(10^{-3}) s$. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved $>20\%$ with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work