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This paper presents the Energy Aware PSO (EAPSO) as a search mechanism for aerial micro-robots with limited energy capacity. The proposed model is an extension of the search concept of Particle Swarm Optimization (PSO) that additionally considers the energy levels of the individuals for an efficient movement. One major contribution of this paper is that the energy efficiency results from a multi-criteria...
In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation...
In this work, we present an extension to the recently developed Integer and Categorical Particle Swarm Optimization (ICPSO), which we refer to as Markovian ICPSO (MICPSO). MICPSO uses a Markov network to represent a particle's position, thus allowing each particle to incorporate information about dependencies between solution variables. In this work, we compare MICPSO to ICPSO, Integer PSO (IPSO),...
A new efficient training algorithm for a Dendrite Morphological Neural Network is proposed. Based on Differential Evolution, the method optimizes the number of dendrites and increases classification performance. This technique has two initialisation ways of learning parameters. The first selects all the patterns and opens a hyper-box per class with a length such that all the patterns of each class...
Over the last two decades, different differential evolution (DE) variants have been successfully used to solve different optimization problems. However, no single DE algorithm has consistently been the best for solving a wide range of them. In the literature, this drawback has been tackled by using multiple DE operators in a single framework. However, utilizing a problem's landscape in the design...
Nowadays, in order to maintain their competitiveness, manufacturing companies must adapt their production methods quickly, with minimum expenditure, to frequent variations on demand. With the shortage of the product life time, flexibility, efficiency and reusability of industrial processes are important factors, which may determine the survival of the company. The ReBORN project is working around...
Todays multi-core architectures with accelerators provide tremendous compute power. Population-based metaheuristic algorithms have proven particularly amenable to single instruction multiple data (SIMD)-style parallelization due to the fine-grained parallelism provided by these algorithms. While SIMD hardware allows one to run large scale simulations, obtaining better solution quality often requires...
Evolutionary process has become a popular design method for experimenting and automatically synthesizing intelligent controllers for autonomous robots. Such controllers are automatically created using different evolutionary methods without direct programming or in-depth human knowledge of the design. Multi-agent systems and collective behaviors based on swarm intelligence observed in nature are generally...
Research in the synthesis of antenna is starting to pay attention to optimize the multiobjective problems. While evolutionary algorithms has obtained some satisfactory performance, there still be necessary to make further improvements especially on the suppression of premature. In this paper, we introduce the multi-swarm technique into the multiobjective particle swarm optimization and present a MOP...
The human brain is structured with the capacity to repair itself. This plasticity of the brain has motivated researchers to develop systems which have similar capabilities of fault tolerance and self-repair. Recent research findings have proven that interactions between astrocytes and neurons can actuate brain-like self-repair in a bidirectionally coupled astrocyte-neuron system. This paper presents...
Automotive networks are simple, real-time networks with very low error rates. In-vehicle infotainment (IVI) systems include GPS-based navigation and wireless hotspots for cellular communication. In the event of a power outage or a major catastrophe, such as an earthquake, existing network mainframes may either shut down completely or become overloaded with traffic. In that regard, it is important...
This paper proposes an incremental mechanism for the automatic recognition of physical activities performed by humans. The specific research field has become quite relevant as it may offer important information to areas such as ambient intelligence, pervasive computing, and assistive technologies. The works in the related literature so far assume the a-priori availability of the dictionary of activities...
In this paper, we propose a privacy preserving protocol for cloud system utilization based on extreme learning machine (ELM). The purpose is to implement aware agents (A-agents) on portable/wearable computing devices (P/WCD). The proposed protocol is useful to reduce the calculation cost on the P/WCD. The basic idea of the protocol is to divide an ELM-based A-agent into two parts, one containing the...
The field of evolutionary robotics shows great promise, but is held back by the lack of results applicable to real world problems or other research fields. The reality gap effects present when moving from virtual to real robots makes evolution based on simulation inefficient for continuous adaption to changing morphology or environments. Evolution on the physical robot does not share these challenges,...
To model intelligent complex systems engineers use the techniques of distributed artificial intelligence and the agent paradigm increasingly However, the problem of decision making by components of a complex system with local, incomplete, uncertain, exchanged or observed in asynchronous manner is often present in agent models. To provide a solution to this problem, studies on quantum cognition introduce...
Evolution-In-Materio, an unconventional computing paradigm exploiting physical properties of materials for achieving computations, is addressed here as a system which exhibits dynamical hierarchies. A description of computations is provided to show that computations within Evolution-In-Materio systems arise from the dynamics at different hierarchical levels. An information theoretic approach to formalising...
This study investigates the impact of genotypic and behavioral diversity maintenance methods on controller evolution in multi-robot (RoboCup keep-away soccer) tasks. The focus is to examine the impact of these methods on the transfer learning of behaviors, first evolved in a source task before being transferred for further evolution in different but related target tasks. The goal is to ascertain an...
Description Logics, defined as a family of knowledge representation languages, have gained a lot of popularity, due to their connection with the Semantic Web, and more precisely, with the Web Ontology Language - OWL (OWL-DL). Vague information cannot be considered negligible when dealing with Semantic Web tasks. In this context, the definition of fuzzy DLs has been emerged. The Semantics of any DL...
Reducing costs whilst maintaining passenger satisfaction is an important problem for airports. One area this can be applied is the security lane checks at the airport. However, reducing costs through reducing lane openings typically increases queue length and hence passenger dissatisfaction. This paper demonstrates that evolutionary methods can be used to optimise airport security lane schedules such...
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