A Survey: Spider Monkey Optimization Algorithm

Swarm intelligence is a one of the areas for evaluating the optimization states. Many algorithms have been developed by simulating the swarming behaviour of various creatures like ants, honey bees, fishes, birds and their results are found as very motivating for solving optimization problems. In this paper, a new approach for optimization is proposed by modelling the social behaviour of spider monkeys. Spider monkeys have been categorized as fission-fusion social structure based animals. The animals which follow fission-fusion social systems, initially work in a large group and based on need after some time, they divide themselves in smaller groups led by an adult female for foraging. There- fore, the proposed strategy broadly classified as inspiration from the intelligent foraging behaviour of fission-fusion social structure based animals

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

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

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA

Enhancement of bees algorithm for global optimisation

This research focuses on the improvement of the Bees Algorithm, a swarm-based nature-inspired optimisation algorithm that mimics the foraging behaviour of honeybees. The algorithm consists of exploitation and exploration, the two key elements of optimisation techniques that help to find the global optimum in optimisation problems. This thesis presents three new approaches to the Bees Algorithm in a pursuit to improve its convergence speed and accuracy. The first proposed algorithm focuses on intensifying the local search area by incorporating Hooke and Jeeves’ method in its exploitation mechanism. This direct search method contains a pattern move that works well in the new variant named “Bees Algorithm with Hooke and Jeeves” (BA-HJ). The second proposed algorithm replaces the randomly generated recruited bees deployment method with chaotic sequences using a well-known logistic map. This new variant called “Bees Algorithm with Chaos” (ChaosBA) was intended to use the characteristic of chaotic sequences to escape from local optima and at the same time maintain the diversity of the population. The third improvement uses the information of the current best solutions to create new candidate solutions probabilistically using the Estimation Distribution Algorithm (EDA) approach. This new version is called Bees Algorithm with Estimation Distribution (BAED). Simulation results show that these proposed algorithms perform better than the standard BA, SPSO2011 and qABC in terms of convergence for the majority of the tested benchmark functions. The BA-HJ outperformed the standard BA in thirteen out of fifteen benchmark functions and is more effective in eleven out of fifteen benchmark functions when compared to SPSO2011 and qABC. In the case of the ChaosBA, the algorithm outperformed the standard BA in twelve out of fifteen benchmark functions and significantly better in eleven out of fifteen test functions compared to qABC and SPSO2011. BAED discovered the optimal solution with the least number of evaluations in fourteen out of fifteen cases compared to the standard BA, and eleven out of fifteen functions compared to SPSO2011 and qABC. Furthermore, the results on a set of constrained mechanical design problems also show that the performance of the proposed algorithms is comparable to those of the standard BA and other swarm-based algorithms from the literature

One-Shot Multi-Winner Self-Organizing Maps

There exist two different approaches to self-organizing maps (SOMs). One approach, rooted in theoretical neuroscience, uses SOMs as computational models of biological cortex. The other approach, taken in computer science and engineering, views SOMs as tools suitable to perform, for example, data visualization and pattern classification tasks. While the first approach emphasizes fidelity to neurobiological data, the latter stresses computational efficiency and effectiveness. In the research reported here, I developed and studied a class of SOMs that incorporates the multiple, simultaneous winner nodes implicit in many biologically-oriented SOMs, but determines the winners using the same efficient one-shot algorithm employed by computationally-oriented, single-winner SOMs. This was achieved by generalizing single-winner SOMs, using localized competitions. The resulting one-shot multi-winner SOM was found to support the formation of multiple adjacent, mirror-symmetric topographic maps. It constitutes the first computational model of mirror-image map formation, and raises questions about the role of Hebbian-type synaptic changes in the formation of mirror-symmetric maps that are often observed in the sensory neocortex of many species, including humans. The model unexpectedly predicted the occasional occurrence of adjacent, rotationally symmetric maps. It is natural to speculate that such atypically oriented maps might contribute to abnormal cortical information processing in some neurodevelopmental disorders. Traditional SOMs lack applicability to problems where the inputs are not single patterns, but temporal sequences of patterns. Several SOM extensions have been proposed as a remedy, but there is no standard for processing temporal sequences with SOMs. I focused on the task of learning unique spatial representations for non-trivial sets of temporal sequences. The one-shot multi-winner SOM extended by temporally-asymmetric Hebbian synapses proved effective when applied to this task. The learned representations retained information about sequence similarity. The feature maps that formed show that temporal sequence processing and map formation are not mutually exclusive. Since the sequence processing one-shot multi-winner SOM was trained with phonetic transcriptions of spoken words, the results can be related to the internalization of spoken words during language acquisition. A final redesign of the network and the subsequent multi-objective optimization of its parameters using a genetic algorithm produced a more effective system

Theoretical Computer Science and Discrete Mathematics

This book includes 15 articles published in the Special Issue "Theoretical Computer Science and Discrete Mathematics" of Symmetry (ISSN 2073-8994). This Special Issue is devoted to original and significant contributions to theoretical computer science and discrete mathematics. The aim was to bring together research papers linking different areas of discrete mathematics and theoretical computer science, as well as applications of discrete mathematics to other areas of science and technology. The Special Issue covers topics in discrete mathematics including (but not limited to) graph theory, cryptography, numerical semigroups, discrete optimization, algorithms, and complexity

Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding

Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management

Ensimmäinen ja toinen käsikirjoitusversio väitöskirjaa varten

This publication contains the first and the second manuscript version for LauriLahti’s doctoral dissertation in 2015 "Computer-assisted learning based on cumulative vocabularies, conceptual networks and Wikipedia linkage".Tämä julkaisu sisältää ensimmäisen ja toisen käsikirjoitusversion Lauri Lahden väitöskirjaan vuonna 2015 "Tietokoneavusteinen oppiminen perustuen karttuviin sanastoihin, käsiteverkostoihin ja Wikipedian linkitykseen".Not reviewe