The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

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

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

Characterising animal foraging behaviour and implications for resource management

The spatial-dynamics of animal movement behaviour are still under-studied and remain less understood than desired. Exploration of this phenomenon leads to important economic, ecological and natural-resource management implications. Yet despite the recent advances in technology and scientific methods, questions remain in terms of understating the complexities of movement patterns and robust quantification. Key factors impeding the investigation have been the lack of accurate data and incisive mathematical and quantification models. Animal movement in general, and foraging in particular, are vital characteristics of species which constantly adapt to changes in physical, biological, and social dynamics. Measuring animal movement patterns poses critical questions surrounding specification of appropriate representations of data generation. Accurate methods that identify underlying patterns from incomplete or imprecise raw data are therefore much desired in movement analysis. A better and deeper understanding of the actual heterogeneous patterns of movement can enable more effective management, conservation and development activities. Since the initial identification of a specific pattern termed Lévy flights in foraging animals by Viswanathan et al. (1996, 1999), many later studies have explored this phenomenon. Lévy flight is a special type of random walk derived from the so-called power-law distribution. A vast and diverse variety of foraging animals have been found to exhibit this movement pattern. However, Edwards (2011) overturned previous conclusions surrounding the existence of Lévy flights within a diverse sample of ecological settings, including five species: reindeer in Sweden (Mårell et al. 2002); side-striped jackals in Zimbabwe (Atkinson et al. 2002); microzooplankton (Bartumeus et al. 2003); grey seals (Austin et al. 2004); and humans in the form of fishers (Bertrand et al. 2007; Marchal et al. 2007) and hunter gatherers (Brown et al. 2007). Re-analysing the above data sets using a modern likelihood approach, Edwards found that Lévy flights pattern is not as common a phenomenon as once thought. The overarching aim of this thesis is to contribute to a better understanding of animal foraging movement patterns, and thus inform the improved management of the landscapes in which iii foragers are found. Central to achieving this aim is the testing of the hypothesis that Lévy flight is not a common phenomenon in nature, through the adaptation and application of two robust Bayesian statistical approaches to a number of distinct data sets. The results obtained through Bayesian approaches are compared with previous findings. Methodologically, this thesis employs the Standard Bayesian estimation approach (SBEA) (likelihood-based Bayesian method) and the Approximate Bayesian Computation (ABC) (likelihood-free Bayesian method), to re-analyse three of the original data sets re-analysed by Edwards (2011). These data sets include; (a) Dobe Ju/’hoansi human hunter-gatherers in Botswana and Namibia (Brown et al. 2007) (b) reindeer in Sweden (Mårell et al. 2002) and (c) Dutch beam-trawler fishing boats (Marchal et al. 2007). Standard Bayesian analysis is dependent on the specification of a likelihood function. For more complex models such as those for identifying movement patterns, specifying a likelihood function is computationally difficult. Therefore, the application of a simulation-based likelihood-free ABC method provides additional precision and robustness. Results reveal that irrespective of the species or foraging objective, humans in the form of hunter gathers and fishers, as well as reindeer, exhibit a bounded Lévy flight foraging pattern. This finding disproves and simultaneously improves the previous findings by Edwards (2011) and other original authors. The thesis also finds that foraging patterns evolve with the availability of prey across time, which is in par with earlier studies. In terms of the methodology, comparing the two Bayesian techniques, the thesis concludes that the likelihood-free Bayesian framework is better able to capture the underlying patterns of animal movement compared to the conventional approaches. This is a crucial finding specifically in terms of animal movement exploration where there is a lack of precise and complete data. Rather than simply assume that a Bayesian approach is “better”, in this thesis the robustness and relevance of using Bayesian approaches is then further explored through a number of simulations and applications. First, movement patterns are simulated to ensure that the two Bayesian methods do indeed recover the true movement pattern. Second, one of the datasets used is progressively truncated to determine how sensitive these methods are to the number of data observations. Third, a number of applications of these methods of relevance to resource management are discussed in the thesis, for which improvement and further modifications to Bayesian simulation-based methods allow more efficient and accurate investigations: wildlife iv corridors developed to minimise the negative impacts of fragmented landscapes; and optimal containment of invasive species. In each case, policy makers require improved understanding of how species move and the rate of spread of species, respectively, often when there is little available data

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

Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing

Applied Methuerstic 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

New nature-inspired metaheuristics applied to the constrained optimization of a heavy-duty gas turbine operation

Orientador: Prof. Dr. Leandro dos Santos CoelhoTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 26/11/2020Inclui referências: p. 94-107Área de concentração: Sistemas EletrônicosResumo: Os codigos computacionais complexos das mais diversas areas, tais como industria 4.0 e energia, apresentam caracteristicas como nao-linearidade, escala, multimodalidade e presenca de restricoes. Por este motivo, as tecnicas classicas Newtonianas e baseadas em gradiente nao sao recomendadas para problemas de otimizacao global, os quais contem inumeras variaveis de projeto, restricoes e simulacoes incorporadas. Isso incentivou novas pesquisas em metaheuristicas baseadas em fenomenos naturais, principalmente comportamentos de animais com caracteristicas cooperativas ou colaborativas. Entretanto, nao existe um algoritmo unico capaz de ter bom desempenho para todos os tipos de problemas de otimizacao, o que justifica a busca recorrente por novas abordagens para solucionar esses problemas. Portanto, a presente tese introduz duas metaheuristicas com estruturas inovadoras inspiradas na natureza e nunca propostas. A primeira e baseada na especie Canis latrans e denominada Algoritmo de Otimizacao dos Coiotes (do ingles Coyote Optimization Algorithm, COA). A segunda, por sua vez, e inspirada na especie Cebus capucinus e denominada Otimizador dos Macacos-prego-da-cara-branca (do ingles Whitefaced Capuchin Monkeys Optimizer, WfCMO). Os algoritmos propostos sao avaliados sob um conjunto de funcoes de benchmarks empregadas nas competicoes do Congresso de Computacao Evolutiva (do ingles Congress on Evolutionary Computation, CEC) organizado pelo Instituto de Engenheiros Eletricistas e Eletronicos (do ingles Institute of Electrical and Electronics Engineers, IEEE) e comparadas a outras metaheuristicas inspiradas na natureza. Alem disso, a modelagem de um problema de otimizacao com restricoes de uma turbina a gas do tipo heavy-duty de uma termeletrica brasileira tambem e proposto nesta pesquisa. Para soluciona-lo, uma versao cultural do COA e proposta e seu desempenho e avaliado e comparado com outros algoritmos do estado-da-arte. Os resultados mostram que as metaheuristicas propostos nesta pesquisa alcancaram desempenho satisfatorio e superaram os outros algoritmos com 95% de confianca estatistica com base no teste nao-parametrico deWilcoxon-Mann-Whitney e tambem nos criterios do IEEE CEC 2017. Ainda, os resultados conquistados para problems multimodais e de alta dimensao mostram que as tecnicas sao promissoras para estes tipos de problema, que sao usuais em problemas reais. Ademais, as analises de curva de convergencia e de diversidade da populacao indicam um balanco adequado entre exploracao e aproveitamento. Por fim, a versao cultural do COA, que se demonstrou capaz de evitar convergencia prematura, superou os demais algoritmos do estado-da-arte para o problema de otimizacao da operacao da turbina. Palavras-chave: Industria 4.0, Inteligencia Computacional, Otimizacao Global, Metaheuristicas inspiradas na natureza.Abstract: The real-world applications from the most diverse fields such as industry 4.0 and energy have been formulated into complex computational codes with features as non-linearity, scale, multimodality, and the presence of constraints. Because of that, the classic Newtonians and gradient-based techniques are not recommended for global optimization applications with many design variables, constraints, and simulations embedded. It has encouraged new researches on metaheuristics based on natural phenomena, mainly animal behaviors with cooperative or collaborative features. However, there is not a unique algorithm able to perform well for all types of optimization problems, which justifies the recurrent search for new approaches. Hence, this thesis presents two never-proposed nature-inspired metaheuristics with innovative structures. The first one is based on the Canis latrans species and it is denoted Coyote Optimization Algorithm (COA). The second one is inspired by the Cebus capucinus species and receives the name of White-faced Capuchin Monkeys Optimizer (WfCMO). The proposed algorithms are evaluated under a set of benchmark functions employed in the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) competitions and compared to other state-of-the-art nature-inspired metaheuristics. Besides, the design of a constrained optimization problem of a heavy-duty gas turbine operation from a Brazilian thermoelectric power plant is proposed in this research. To solve it, a cultural version of the COA is proposed and its performance is evaluated and compared to other state-of-the-art algorithms. The results show that the proposed metaheuristics achieve profitable performance and outperform some state-of-the-art algorithms with 95% of statistical confidence based on the Wilcoxon-Mann- Whitney non-parametric test and the criteria of the IEEE CEC of 2017. Also, these algorithms present promising results for multimodal and high dimensional problems, which are the most usual features of real-world problems. Moreover, the convergence and diversity curves indicate a suitable balance between exploration and exploitation. Further, the proposed cultural version of the COA outperforms other state-of-the-art algorithms for the gas turbine operation problem. Its ability to avoid premature convergence is also demonstrated. Keywords: Industry 4.0, Computational Intelligence, Global Optimization, Nature-Inspired Metaheuristics