Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

A Hybrid Meta-Heuristic Feature Selection Method Using Golden Ratio and Equilibrium Optimization Algorithms for Speech Emotion Recognition

Speech is the most important media of expressing emotions for human beings. Thus, it has often been an area of interest to understand the emotion of a person out of his/her speech by using the intelligence of the computing devices. Traditional machine learning techniques are very much popular in accomplishing such tasks. To provide a less expensive computational model for emotion classification through speech analysis, we propose a meta-heuristic feature selection (FS) method using a hybrid of Golden Ratio Optimization (GRO) and Equilibrium Optimization (EO) algorithms, which we have named as Golden Ratio based Equilibrium Optimization (GREO) algorithm. The optimally selected features by the model are fed to the XGBoost classifier. Linear Predictive Coding (LPC) and Linear Prediction Cepstral Coefficients (LPCC) based features are considered as the input here, and these are optimized by using the proposed GREO algorithm. We have achieved impressive recognition accuracies of 97.31% and 98.46% on two standard datasets namely, SAVEE and EmoDB respectively. The proposed FS model is also found to perform better than their constituent algorithms as well as many well-known optimization algorithms used for FS in the past. Source code of the present work is made available at: https://github.com/arijitdey1/Hybrid-GREO

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

An Evolutionary Optimization Algorithm for Automated Classical Machine Learning

Machine learning is an evolving branch of computational algorithms that allow computers to learn from experiences, make predictions, and solve different problems without being explicitly programmed. However, building a useful machine learning model is a challenging process, requiring human expertise to perform various proper tasks and ensure that the machine learning\u27s primary objective --determining the best and most predictive model-- is achieved. These tasks include pre-processing, feature selection, and model selection. Many machine learning models developed by experts are designed manually and by trial and error. In other words, even experts need the time and resources to create good predictive machine learning models. The idea of automated machine learning (AutoML) is to automate a machine learning pipeline to release the burden of substantial development costs and manual processes. The algorithms leveraged in these systems have different hyper-parameters. On the other hand, different input datasets have various features. In both cases, the final performance of the model is closely related to the final selected configuration of features and hyper-parameters. That is why they are considered as crucial tasks in the AutoML. The challenges regarding the computationally expensive nature of tuning hyper-parameters and optimally selecting features create significant opportunities for filling the research gaps in the AutoML field. This dissertation explores how to select the features and tune the hyper-parameters of conventional machine learning algorithms efficiently and automatically. To address the challenges in the AutoML area, novel algorithms for hyper-parameter tuning and feature selection are proposed. The hyper-parameter tuning algorithm aims to provide the optimal set of hyper-parameters in three conventional machine learning models (Random Forest, XGBoost and Support Vector Machine) to obtain best scores regarding performance. On the other hand, the feature selection algorithm looks for the optimal subset of features to achieve the highest performance. Afterward, a hybrid framework is designed for both hyper-parameter tuning and feature selection. The proposed framework can discover close to the optimal configuration of features and hyper-parameters. The proposed framework includes the following components: (1) an automatic feature selection component based on artificial bee colony algorithms and machine learning training, and (2) an automatic hyper-parameter tuning component based on artificial bee colony algorithms and machine learning training for faster training and convergence of the learning models. The whole framework has been evaluated using four real-world datasets in different applications. This framework is an attempt to alleviate the challenges of hyper-parameter tuning and feature selection by using efficient algorithms. However, distributed processing, distributed learning, parallel computing, and other big data solutions are not taken into consideration in this framework

A tutorial for competent memetic algorithms: Model, taxonomy and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Some improved genetic-algorithms based heuristics for global optimization with innovative applications

The research is a study of the efficiency and robustness of genetic algorithm to instances of both discrete and continuous global optimization problems. We developed genetic algorithm based heuristics to find the global minimum to problem instances considered. In the discrete category, we considered two instances of real-world space allocation problems that arose from an academic environment in a developing country. These are the university timetabling problem and hostel space allocation problem. University timetabling represents a difficult optimization problem and finding a high quality solution is a challenging task. Many approaches, based on instances from developed countries, have been reported in the literature. However, most developing countries are yet to appreciate the deployment of heuristics and metaheuristics in handling the timetabling problem. We therefore worked on an instance from a university in Nigeria to show the feasibility and efficiency of heuristic method to the timetabling problem. We adopt a simplified bottom up approach in which timetable are build around departments. Thus a small portion of real data was used for experimental testing purposes. As with similar baseline studies in literature, we employ genetic algorithm to solve this instance and show that efficient solutions that meet stated constraints can be obtained with the metaheuristics. This thesis further focuses on an instance of university space allocation problem, namely the hostel space allocation problem. This is a new instance of the space allocation problems that has not been studied by metaheuristic researchers to the best of our knowledge. The problem aims at the allocation of categories of students into available hostel space. This must be done without violating any hard constraints but satisfying as many soft constraints as possible and ensuring optimum space utilization. We identified some issues in the problem that helped to adapt metaheuristic approach to solve it. The problem is multi-stage and highly constrained. We first highlight an initial investigation based on genetic algorithm adapted to find a good solution within the search space of the hostel space allocation problem. Some ideas are introduced to increase the overall performance of initial results based on instance of the problem from our case study. Computational results obtained are reported to demonstrate the effectiveness of the solution approaches employed. Sensitivity analysis was conducted on the genetic algorithm for the two SAPs considered to determine the best parameter values that consistently give good solutions. We noted that the genetic algorithms perform well specially, when repair strategies are incorporated. This thesis pioneers the application of metaheuristics to solve the hostel space allocation problem. It provides a baseline study of the problem based on genetic algorithms with associated test data sets. We report the best known results for the test instances. It is a known fact that many real-life problems are formulated as global optimization problems with continuous variables. On the continuous global optimization category therefore, we focus on improving the efficiency and reliability of real coded genetic algorithm for solving unconstrained global optimization, mainly through hybridization with exploratory features. Hybridization has widely been recognized as one of the most attractive approach to solving unconstrained global optimization. Literatures have shown that hybridization helps component heuristics to taking advantage of their individual strengths while avoiding their weaknesses. We therefore derived three modified forms of real coded genetic algorithm by hybridizing the standard real-coded genetic algorithm with pattern search and vector projection. These are combined to form three new algorithms namely, RCGA-PS, RCGA-P, and RCGA-PS-P. The hybridization strategy used and results obtained are reported and compared with the standard real-coded genetic algorithm. Experimental studies show that all the modified algorithms perform better than the original algorithm

Enhanced evolutionary algorithm with cuckoo search for nurse scheduling and rescheduling problem

Nurse shortage, uncertain absenteeism and stress are the constituents of an unhealthy working environment in a hospital. These matters have impact on nurses' social lives and medication errors that threaten patients' safety, which lead to nurse turnover and low quality service. To address some of the issues, utilizing the existing nurses through an effective work schedule is the best alternative. However, there exists a problem of creating undesirable and non-stable nurse schedules for nurses' shift work. Thus, this research attempts to overcome these challenges by integrating components of a nurse scheduling and rescheduling problem which have normally been addressed separately in previous studies. However, when impromptu schedule changes are required and certain numbers of constraints need to be satisfied, there is a lack of flexibility element in most of scheduling and rescheduling approaches. By embedding the element, this gives a potential platform for enhancing the Evolutionary Algorithm (EA) which has been identified as the solution approach. Therefore, to minimize the constraint violations and make little but attentive changes to a postulated schedule during a disruption, an integrated model of EA with Cuckoo Search (CS) is proposed. A concept of restriction enzyme is adapted in the CS. A total of 11 EA model variants were constructed with three new parent selections, two new crossovers, and a crossover-based retrieval operator, that specifically are theoretical contributions. The proposed EA with Discovery Rate Tournament and Cuckoo Search Restriction Enzyme Point Crossover (DᵣT_CSREP) model emerges as the most effective in producing 100% feasible schedules with the minimum penalty value. Moreover, all tested disruptions were solved successfully through preretrieval and Cuckoo Search Restriction Enzyme Point Retrieval (CSREPᵣ) operators. Consequently, the EA model is able to fulfill nurses' preferences, offer fair on-call delegation, better quality of shift changes for retrieval, and comprehension on the two-way dependency between scheduling and rescheduling by examining the seriousness of disruptions