A new metaheuristic Bat-inspired Algorithm

Mohammed Al-Weshah

doi:10.1007/978-3-642-12538-6_6

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A new metaheuristic Bat-inspired Algorithm

Mohammed Al-Weshah

2010, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)

https://doi.org/10.1007/978-3-642-12538-6_6

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10 pages

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Abstract

Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.

FAQs

What advantages does the Bat Algorithm offer compared to existing metaheuristic methods?add

The Bat Algorithm demonstrates superior accuracy and efficiency, outperforming both particle swarm optimization and genetic algorithms. Specifically, it combines the strengths of traditional algorithms while leveraging the unique echolocation behavior of microbats.

How does the pulse emission rate affect the optimization process in the Bat Algorithm?add

The pulse emission rate, varying between 0 and 1, influences the local search intensity, with increased rate upon nearing a target. The study suggests that adjusting this parameter can significantly impact the convergence rate.

What role does the frequency and wavelength play in the Bat Algorithm's functioning?add

Frequency and wavelength adjustments in the Bat Algorithm relate directly to the bats' distance sensing capabilities and solution exploration range. Specific choices, such as a frequency range of 0 to 100, provide flexibility for optimizing various problems.

What benchmarks were used to validate the performance of the Bat Algorithm?add

Benchmark functions such as Rosenbrock's function and the Eggcrate function were employed to evaluate the Bat Algorithm. These functions were chosen due to their established global optima, facilitating meaningful performance comparisons against other algorithms.

What implications does the study suggest for future research on the Bat Algorithm?add

The study indicates the need for further sensitivity analyses and advanced comparisons with a broader array of algorithms. Future work may explore more complex variations of echolocation features and their potential impacts on algorithm performance.

Figures (3)

Figure 2: The paths of 25 virtual bats during 20 consecutive iterations. They converge into (1, 1):

Figure 3: The eggcrate function (left) and the locations of 40 bats in the last ten iterations (right).

Table 1: Comparison of BA with GA, and PSO. From Table 1, we can see that PSO performs much better than genetic algorithms, while the Bat Algorithm is much superior to other algorithms in terms of accuracy and efficiency. This is no surprising as the aim of developing the new algorithm was to try to use the advantages of existing algorithms and other interesting feature inspired by the fantastic behaviour of echolocation of microbats.

References (15)

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A recent set of overused population-based methods have been published in recent years. Despite their popularity, as a result of manipulated systematic internet marketing, product bundling, and advertising techniques, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar immature exploration and exploitation components and operations, and an insecure tradeoff between exploration and exploitation trends in most of the new real-world cases. Therefore, all users need to extensively modify and adjust their operations based on main evolutionary methods to reach faster convergence, more stable balance, and high-quality results. To move the optimization community one step ahead toward more focus on performance rather than change of metaphor, a general-purpose population-based optimization technique called Hunger Games Search (HGS) is proposed in this research with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. The proposed HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of "Hunger" as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. The effectiveness of HGS was verified by comparing HGS with a comprehensive set of popular and advanced algorithms on 23 well-known optimization functions and the IEEE CEC 2014 benchmark test suite. Also, the HGS was applied to several engineering problems to demonstrate its applicability. The results validate the effectiveness of the proposed optimizer compared to popular essential optimizers, several advanced variants of the existing methods, and several CEC winners and powerful differential evolution (DE)based methods abbreviated as LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods in handling many single-objective problems. We designed this open-source population-based method to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity. The method is very flexible and scalable to be extended to fit more form of optimization cases in both structural aspects and application sides. This paper's source codes, supplementary files, Latex and office source files, sources of plots, a brief version and pseudocode, and an opensource software toolkit for solving optimization problems with Hunger Games Search and https://aliasgharheidari.com/HGS.html

Este trabalho reflete toda a minha vivência e crescimento enquanto pessoa e gestor desportivo, sem o qual não poderia deixar de agradecer às pessoas mais próximas que permitiram que conseguisse atingir este patamar. Em primeiro lugar, gostaria de agradecer à Orientadora da Faculdade, Prof.ª Doutora Maria José Carvalho, por todos os sábios conselhos, por toda a partilha de experiências e por todas as críticas construtivas que me fizeram crescer enquanto profissional. Ao orientador do clube, o Prof. José Magalhães, por me permitir viver esta experiência e por servir como um guia em todo o processo do meu estágio. Ao Armando Leitão, o team manager da equipa A de séniores do clube, por todos os momentos vividos, por acreditar no meu trabalho e não ter medo de me deixar assumir muitas das tarefas que tivemos pela frente. Sem dúvida, que sem ele não tinha aprendido metade do que aprendi. Aos elementos do clube que pertencem ao Andebol, por me receberem tão bem e me fazerem sentir tão bem como já pertencesse ali há bastante tempo. Foram tantas as amizades criadas que nunca me esquecerei dos momentos passados. Aos restantes elementos do clube, com os quais lidei, mesmo não estando estes ligados ao andebol, sempre tiveram uma grande recetividade e compreensão com a minha função. Não posso deixar de agradecer à minha família, que foi o suporte mais importante para aqui chegar. Apoiaram-me sempre nas minhas decisões, criticaram-me sempre que agi mal e chatearam-me sempre para fazer um bom trabalho em tudo o que fazia. Educaram-me, com valores e uma ambição desmedida. À minha mãe, por herdar dela o gosto pelo Desporto e pelo ensino desta área a crianças. Um agradecimento especial por me introduzir no Desporto desde criança, que é a minha vida. VI Ao meu pai, por me dar a conhecer os Escuteiros e por procurar que eu fosse sempre bom no que fazia. Ao meu irmão, por todos os momentos passados a seu lado, desde discussões a brincadeiras, por todo o apoio dado ao longo da vida. À minha avó Carmem, por todo o carinho e todo o amor que me deu ao longo da vida. À minha tia Lola, por todo o conhecimento que me transmitiu e por me dar a conhecer uma das paixões da minha vida, Viajar! À minha tia Zira, por todo o apoio e carinho que me deu ao longo do percurso. À minha prima Ana, por ser um exemplo de empenho, dedicação e de lutar pelos objetivos que desejámos na vida. Um agradecimento especial, à Catarina, minha namorada, por fazer parte da minha vida já há muito tempo e ter percorrido a meu lado, todo o percurso de formação para professor. Por me aturar nos momentos difíceis, pelo seu sorriso contagiante e pela felicidade inestimável nos grandes momentos que passamos juntos. Por ser uma mais-valia no apoio e ajuda na elaboração deste relatório. Por fim, quero agradecer a todas as pessoas que passaram pela minha vida e que me moldaram na pessoa que sou hoje. Desde os meus amigos mais próximos, a companheiros de equipa, a colegas dos escuteiros, a professores na escola e treinadores no Andebol. A todos vocês o meu sincero OBRIGADO. VII Índice AGRADECIMENTOS .

This paper presents an innovative hybrid algorithm (ihPSODE) for economic load dispatch (ELD) problems with or without valve-point effects. It integrated with suggested novel PSO (nPSO) and DE (nDE). Where in nPSO, a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE, a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of ihPSODE population, best half member has been identified and rest members discarded. Further, in current population nPSO is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed ihPSODE and its suggested component nPSO and nDE have been tested over IEEE CEC2006 (it comprising linear, nonlinear, polynomial, quadratic, and cubic functions), IEEE CEC2014, and IEEE CEC2017 (these test suites consist of unimodal, multimodal, hybrid, and composition functions) well-known benchmark functions. Then, the efficacy of the proposed algorithms has been demonstrated on nonconvex economic load dispatch problems which consist of 3-, 6-, 15-, 40-, and 140-unit test systems. The comparative results with some of the state-of-the-art algorithms confirm the effectiveness of the proposed algorithms in both aspects of better quality solutions and higher efficiency. K E Y W O R D S benchmark function, differential evolution, economic load dispatch, hybrid algorithm, particle swarm optimization Year Author and references Algorithm Brief description Application 2006 He and Han 41 PSO-DT In PSO-DT, a disturbance term added in velocity updates formula of the PSO which effectively mends the defects. Unconstrained problems 2007 Yang et al 42 DAPSO A dynamically changed inertia weight introduced in DAPSO which enhances the convergence precision and local optima ability of PSO significantly. Unimodal and multimodal function 2008 Jie et al 43 KCPSO It introduced multiswarm (to maintain diversity) and knowledge billboard (to record varieties of search information) mechanism. Complex multimodal functions 2009 Cai, Cui, and Tan 44 PMPSO-TVAC Social and cognitive learning factors of PSO adjusted according to a predefined predicted velocity index in PMPSO-TVAC. Multimodal highdimensional numerical problems

Розроблене інструментальне середовище для визначення часової та функціональної ефективності алгоритмів. Передбачені можливості дослідження ефективності алгоритмів на множині особливих «незручних» функцій, яку можливо змінювати та доповнювати. Виконані комп'ютерні експерименти з визначенням теоретичних засад, підготовчих заходів, реалізацією та аналізом отриманих результатів. Отримані залежності часової та функціональної ефективності зграйного алгоритму від кількості параметрів функцій, глобальний екстремум яких визначається, та параметрів зграйного алгоритму: розміру популяцій та кількості епох. У розробленому середовищі передбачена можливість дослідження інших біонічних алгоритмів. Ключові слова: часова ефективність, функціональна ефективність, біонічний алгоритм, комп'ютерний експеримент. Разработана инструментальная среда для определения временной и функциональной эффективности алгоритмов. Предусмотрены возможности исследования эффективности алгоритмов на множестве особых «неудобных» функций, которо е можно изменять и дополнять. Выполнены компьютерные эксперименты, включая определение теоретических основ, подготовку, реализацию и анализ результатов. Получены зависимости временной и функциональной эффективности роевого алгоритма от количества параметров функций, глобальный экстремум которых определяется, и параметров роевого алгоритма: размера популяций и количества эпох. В разработанной среде предусмотрена возможность исследования других бионических алгоритмов. Ключевые слова: временная эффективность, функциональная эффективность, бионический алгоритм, компьютерный эксперимент. An instrumental environment for determining the time and functional efficiency of algorithms are developed. Abilities of studying the effectiveness of algorithms on a set of special "uncomfortable" functions, which can be changed and implemented are provided. Computer experiments are carried out, including the definition of theoretical foundations, preparation, implementation and analysis of the results. The dependency of the time and functional efficiency of the rouge algorithm on the number of parameters of functions whose global extremum is determined, and the parameters of the roaming algorithm: population size and number of epochs are obtained. In the developed environment, it is possible to study other bionic algorithms.

A new metaheuristic Bat-inspired Algorithm

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