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This paper presents a review of the development of biologically inspired algorithms (BIAs), including the evolutionary algorithms and swarm intelligence, as well as the newly emerged bacterial foraging algorithms. Our retrospectiv...
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This paper presents a review of the development of biologically inspired algorithms (BIAs), including the evolutionary algorithms and swarm intelligence, as well as the newly emerged bacterial foraging algorithms. Our retrospective review is classified to these three areas of BIAs, and their common features and functionalities are discussed respectively. In order to identify the roots and clarify the variants of various BIAs, our discussion is also extended to identify the difference between each algorithm, particularly by indicating their biological background. The review focuses on not only the background of the original studies of BIAs but also their principles and applications, which are supported with a number of references included in this paper.
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Utilization of redundancy endows systems with dexterity and fault tolerance in achieving its desired output, but its utilization implies added complication which must be resolved. Input optimization using the two-norm and the infi...
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Utilization of redundancy endows systems with dexterity and fault tolerance in achieving its desired output, but its utilization implies added complication which must be resolved. Input optimization using the two-norm and the infinity-norm are two methods popularly utilized to address this problem. However, each resolution criteria finds its greatest benefit in opposite circumstances. This fact has long motivated a resolution system which makes use of both norms, switching back and forth when called for. In a previous publication, we introduced and proved the continuity of the first realization of such a switching resolution system, implemented in the resolution of biarticular actuation redundancy. In this work, we demonstrate the empirical validity and utility of the switching system through implementation in a developed robotic arm equipped with biarticular actuation redundancy. It is found that resolution allows for equivalent maximum torque requirements as the infinity-norm - an improvement over two-norm - but with improved electrical energy requirements over infinity-norm.
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In this paper, a novel bio-inspired and nature-inspired algorithm, named dominion algorithm, is proposed for solving optimisation tasks. The fundamental concepts and ideas which underlie the proposed algorithm is inspired from nat...
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In this paper, a novel bio-inspired and nature-inspired algorithm, named dominion algorithm, is proposed for solving optimisation tasks. The fundamental concepts and ideas which underlie the proposed algorithm is inspired from nature and based on the observation of the social structure and collective behaviour of wolf pack in the real world. Several experiments were preformed to evaluate the proposed algorithm and examine the correlation between its main parameters.
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In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic ...
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In this paper, we show the functional similarities between Meta-heuristics and the aspects of the science of life (biology): (a) Meta-heuristics based on gene transfer: Genetic algorithms (natural evolution of genes in an organic population), Transgenic Algorithm (transfers of genetic material to another cell that is not descending); (b) Meta-heuristics based on interactions among individual insects: Ant Colony Optimization (on interactions among individuals insects, Ant Colonies), Firefly algorithm (fireflies of the family Lampyridze), Marriage in honey bees Optimization algorithm (the process of reproduction of Honey Bees), Artificial Bee Colony algorithm (the process of recollection of Honey Bees); and (c) Meta-heuristics based on biological aspects of alive beings: Tabu Search Algorithm (Classical Conditioning on alive beings), Simulated Annealing algorithm (temperature control of spiders), Particle Swarm Optimization algorithm (social behavior and movement dynamics of birds and fish) and Artificial Immune System (immunological mechanism of the vertebrates).
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In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. T...
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In recent times, several new metaheuristic algorithms based on natural phenomena have been made available to researchers. One of these is that of the Krill Herd Algorithm (KHA) procedure. It contains many interesting mechanisms. The purpose of this article is to compare the KHA optimization algorithm used for learning an artificial neural network (ANN), with other heuristic methods and with more conventional procedures. The proposed ANN training method has been verified for the classification task. For that purpose benchmark examples drawn from the UCI Machine Learning Repository were employed with Classification Error and Sum of Square Errors being used as evaluation criteria. It has been concluded that the application of KHA offers promising performance-both in terms of aforementioned metrics, as well as time needed for ANN training.
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Many municipal solid waste management decision-making applications contain considerable elements of stochastic uncertainty. Simulation-optimisation techniques can be adapted to model a wide variety of problem types in which system...
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Many municipal solid waste management decision-making applications contain considerable elements of stochastic uncertainty. Simulation-optimisation techniques can be adapted to model a wide variety of problem types in which system components are stochastic. The family of optimisation methods referred to as simulation-optimisation incorporate stochastic uncertainties expressed as probability distributions directly into their computational procedures. In this paper, a new simulation-optimisation approach is presented that implements a modified version of the computationally efficient, nature-inspired firefly algorithm (FA). The effectiveness of this stochastic FA-drivcn simulation-optimisation procedure for optimisation is demonstrated using a municipal solid waste management case study.
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This paper presents a paradigm for extending network life by introducing energy-aware mobility in wireless sensor networks. The concept of controlled mobility for energy saving has been motivated by a natural grouping behavior tha...
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This paper presents a paradigm for extending network life by introducing energy-aware mobility in wireless sensor networks. The concept of controlled mobility for energy saving has been motivated by a natural grouping behavior that is observed in Emperor penguins in the Antarctic region. During the winters, a group of those penguins form a closely huddled group to improve their collective heat insulation. Individual penguins within a group exhibit a local mobility pattern that ensures that each individual spends roughly equal amount of time at the periphery of the group, where heat loss is the maximum. As a result, the total heat loss of the group is thought to be evenly distributed across all the individuals. In this paper, we first draw a parallel between the heat loss of a penguin at the group periphery, and the routing energy burden of sensor nodes near the aggregating base stations in a sensor network. Then we develop a fully distributed and localized mobility control algorithm for collective extension of the network life. Experimental results demonstrate that the proposed controlled mobility paradigm can significantly extend a sensor network's operating life even in situations where the energy cost of physical node movement is modeled as high as up to four orders of magnitude larger than the energy cost for packet transmission.
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The human mind is a complex biological member with unique abilities. Today, even the most advanced computers cannot do all the brain's calculations in one second. The mind is teachable, and its logic will change based on what it h...
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The human mind is a complex biological member with unique abilities. Today, even the most advanced computers cannot do all the brain's calculations in one second. The mind is teachable, and its logic will change based on what it has learned over time. This behavior will be the base of a theory presented in this article, named Incomprehensible but Intelligible-in-time (IbI) Logics. From a mental point of view and according to human knowledge, what it has not learned is a non-logic. However, this may change through time. Meanwhile, the IbI logic is a non-logic that may become an obvious logic in the future. This article has been formed to introduce a side of science to identify IbI logic and organize the mind's scientific idioms. Based on the introduced theory, a new optimization algorithm called IbI Logics Algorithm (ILA) was also presented and compared with some other algorithms. The performance of the proposed algorithm in several constrained and unconstrained examples were evaluated. The results showed the acceptable performance and potential of the ILA for optimization goals.(c) 2023 Elsevier B.V. All rights reserved.
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A biologically inspired model of head direc tion cells is presented and tested on a small mobile robot. Head direction cells (discovered in the brain of rats in 1984) encode the head orientation of their host irrespective of the h...
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A biologically inspired model of head direc tion cells is presented and tested on a small mobile robot. Head direction cells (discovered in the brain of rats in 1984) encode the head orientation of their host irrespective of the host's location in the environment. The head direction system thus acts as a biological compass (though not a magnetic one) for its host. Head direction cells are influenced in different ways by idio thetic (host-centred) and allothetic (not host-centred) cues. The model presented here uses the visual, vestibu lar and kinesthetic inputs that are simulated by robot sensors. Real robot-sensor data has been used in order to train the model's artificial neural network connec tions. The main contribution of this paper lies in the use of an evolutionary algorithm in order to determine the values of parameters that determine the behaviour of the model. More importantly, the objective function of the evolutionary strategy used takes into consideration quantitative biological observations reported in the literature.
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In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum dista...
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In this paper, a novel biologically-inspired algorithm, namely krill herd (KH) is proposed for solving optimization tasks. The KH algorithm is based on the simulation of the herding behavior of krill individuals. The minimum distances of each individual krill from food and from highest density of the herd are considered as the objective function for the krill movement. The time-dependent position of the krill individuals is formulated by three main factors: (ⅰ) movement induced by the presence of other individuals (ⅱ) foraging activ ity, and (ⅲ) random diffusion. For more precise modeling of the krill behavior, two adap tive genetic operators are added to the algorithm. The proposed method is verified using several benchmark problems commonly used in the area of optimization. Further, the KH algorithm is compared with eight well-known methods in the literature. The KH algo rithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.
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