Artificial Swarm Research Articles

Artificial swarming: Towards radiofrequency control of reversible micro-particle aggregation and deposition

The ability to undergo a transition between dispersed or single-cellular state and aggregated or multi-cellular state provides distinct evolutionary advantages to many natural organisms. Due to a change of hydrodynamic diameter over several orders of magnitude and associated change of fluid–particle interaction (settling velocity) or intra-particle transport phenomena (heat transfer and/or diffusion) that typically scales with the square of the particle size, radically different behaviour can be achieved in terms of transport in a fluid environment, sourcing nutrition, escaping predators or maintaining homeostasis. In this work we report on the implementation of an artificial system that is able to undergo a reversible transition between dispersed and aggregated state, using the principles of “remote control” by radiofrequency (RF) signals. The individual artificial cells are represented by hollow-core SiO2/Fe3O4/PNIPAM microparticles with a stimuli-responsive porous shell that possess the following functionalities: (i) RF-induced local particle heating, due to the presence of superparamegnetic nanoparticles in the structure; (ii) temperature switchable storage/release functionality due to a combination of hollow-core porous silica skeleton with a PNIPAM layer; and (iii) temperature switchable aggregation, due to the hydrophilic/hydrophobic transition of the PNIPAM layer. The combination of RF-switchable aggregation and temperature-responsive release kinetics of a lipophilic substance makes it possible to trigger particle aggregation and deposition remotely, and thus control the release kinetics of encapsulated payload in both time and space.

蜂群算法在光伏电池双二极管五参数模型中的应用

蜂群算法在光伏电池双二极管五参数模型中的应用

Optimal PID control of DC motor with ABC and PSO algorithms

Proportional-integral-derivative (PID) control is widely used in DC motor system, because of its simple structure, high reliability and so on. However, the parameters of PID controller are commonly tuned by trial and error which also has large overshoot phenomenon for nonlinear and time-varying systems. In order to overcome the deficiency of the PID controller whose parameters are difficult to adjust, two swarm intelligence algorithms - artificial bee colony (ABC) and particle swarm optimisation (PSO) - have been proposed to estimate the optimal parameters of PID controller for speed control. The simulations have been done and results show that ABC and PSO algorithms have stable convergence characteristic and good computational ability, and they are effective and easily implemented methods for optimal tuning of PID parameters.

An effort to select a preferable metaheuristic model for knowledge discovery in data mining

Swarm intelligence computation is becoming an important topic in the field of decision making, and is successfully applied in many fields, which is indicating a fairly great potential for development. Recently, much research work has been carried out by swarm intelligence in different fields viz. sensor network, robotic management, microwave filter design, data mining applications etc. In this paper, an effort has been made to make a comparison on the performance of fuzzy logic with different membership function, artificial neural network and swarm intelligence models. The models with minimum errors have been given preference for selection towards the decision making of information. The same methods can also be cross-checked by residual analysis and a new unknown data set to verify the earlier proposed observation. Initially the statistical techniques have been applied on the data set to select the preferable models but it has been observed that statistical technique errors are large. Therefore, under the fuzzy logic, different membership functions have been used to select the preferable membership function for fuzzy logic for the available data. In this paper further effort has also been made to change the expression of the computing fitness function of the path in artificial bee colony to achieve the best result.

Hybridization of ABC and PSO for Optimal Rule Extraction from Knowledge Discovery Database

Knowledge discovery in database (KDD) has provided a large interest in statistics, machine learning, and artificial intelligence (AI). It is a challenging task for mining the comprehensive and informative knowledge in such complex data by using the existing methods. The challenges come from many aspects, for instance, the traditional methods usually discover homogeneous features from a single source of data while it is not effective to mine for patterns combining components from multiple data sources. It is often very costly and sometimes impossible to join multiple data sources into a single data set for pattern mining. In order to extract knowledge from different datasets, we will propose a hybrid mining technique. The knowledge extraction can be done by association rule mining with the combination of Artificial Bee Colony optimization algorithm (ABC) and Particle swarm optimization (PSO). The main aim of this hybridization is to extract the optimal rules from the association rules for further classification. The accuracy will be checked in terms of optimal rule obtained from the hybridization. The best position for moving the particle will be updated by using ABC algorithm.

Channel assignment based on bee algorithms in multi‐hop cognitive radio networks

Spectrum management policies are responsible for poor utilisation of the radio spectrum. By carrying out dynamic spectrum management (DSM), cognitive radio (CR) can increase the radio spectrum in wireless systems efficiently. CR technology accounts for the improvement in the spectrum utilisation significantly. One issue of DSM in CR is the assignment of frequency channels among its users. Herein, a general model and four utility functions for optimal channel assignment in open spectrum systems such as CR networks have been defined. First, a new utility function with a better fairness than the other functions is proposed. Then, two new different channel assignment methods, based on the artificial bee colony (ABC) and bee swarm optimisation (BSO) algorithms, are proposed, whereas other certain evolutionary algorithms and colour sensitive graph colouring (CSGC) are used to compare the performances. In order to decrease the search space, based on the channel availability and interference constraints a mapping process between the channel assignment matrix and the position of the bees has been proposed. Our simulation results, compared to the optimal solutions, show that our algorithms drastically improve network performance by reducing interference.

Regional optimal allocation for reducing waste loads via artificial neural network and particle swarm optimization: a case study of ammonia nitrogen in Harbin, northeast China.

Cutting external waste loads can improve water quality. Allocation for reducing waste loads should consider changing variables, such as river flows and pollutant emissions. A particle swarm optimization (PSO) method and coupling artificial neural network (ANN) models have been applied to optimize reduction rates of ammonia nitrogen (NH3-N) loads from sewage outlets in Harbin, northeast China. For the planned water quality functional section (WQFS), the NH3-N concentration is related to emitted pollutant loads and can be well predicted by ANN linkage models. Further, NH3-N load reduction rates of all outlets are optimized by PSO with the water quality standard target. The highest NH3-N concentrations occur in January and February, a typical low-flow period in Harbin. The results delivered optimum NH3-N reduction rates for the five outlets, for January and February 2011. All predicted NH3-N concentrations after the reduction meet the water quality standard. The results indicate that the outlet with the highest NH3-N load has the biggest reduction rate in each WQFS, and outlets in the WQFS with higher background NH3-N concentrations need to cut more NH3-N loads. Decision-makers should not only focus on the outlet with the highest NH3-N emission load, but also ensure that the NH3-N concentration of upper WQFS meets the water quality goal.

Robotics. Programmable self-assembly in a thousand-robot swarm.

Self-assembly enables nature to build complex forms, from multicellular organisms to complex animal structures such as flocks of birds, through the interaction of vast numbers of limited and unreliable individuals. Creating this ability in engineered systems poses challenges in the design of both algorithms and physical systems that can operate at such scales. We report a system that demonstrates programmable self-assembly of complex two-dimensional shapes with a thousand-robot swarm. This was enabled by creating autonomous robots designed to operate in large groups and to cooperate through local interactions and by developing a collective algorithm for shape formation that is highly robust to the variability and error characteristic of large-scale decentralized systems. This work advances the aim of creating artificial swarms with the capabilities of natural ones.

Online Persian/Arabic script classification without contextual information

Segmentation accuracy plays a vital role in script recognition process and therefore, research in this area is still fresh. Accordingly, this paper presents a new method for segmenting cursive handwritten Persian/Arabic words. Moreover, this paper proposes a novel strategy to tackle the challenge of recognising the handwritten sub-word as input without contextual information. The recognition strategy is tested with Persian scripts but can be used for both Person and Arabic scripts. Initially, the input data are pre-processed to generate desired data. Later, the proposed segmentation approach fragments the pre-processed data at regular intervals. The discriminative features are extracted as a binary input set for a hybrid of artificial neural networks and particle swarm optimisation (ANN–PSO) classifier to recognize Persian script. Finally, the achieved segmentation and recognition rates are compared with the results reported in the literature. Segmentation and recognition rates thus achieved exhibit promising achievements in the state of art.

Fast approach of Pareto-optimal solution recommendation to multi-objective optimal design of serpentine-channel heat sink

A multi-objective structural design of a serpentine channel heat sink is presented in this paper. In the structural modelling of the heat sink, channel width, fin width, channel height and inlet velocity are defined as the design variables, ‘total thermal resistance’ and the ‘pressure drop’ as the two objectives, subject to constraints of fixed length and width of the heat sink. In this study, a multi-objective artificial swarm fish algorithm with a variable population size using a non-dominated sorting method (MOAFNS) has been developed to handle the optimisation, in which fast approach of Pareto-optimal solution recommendation using the Pareto risk index is proposed to handle the optimal trade-offs between the two conflicting thermal objectives. Then, the optimal solutions have been validated by performing related experiments. The Pareto-front indicates a trade-off between ‘total thermal resistance’ and ‘pressure drop’. Numerical results and experimental data have reached an agreement that reduction in both thermal resistance and pressure drop can be achieved via determination of channel configuration and inlet velocity using MOAFNS, which results in desired thermal performance of the heat sink.

Grand Challenges for Computational Intelligence

The expansive research field of computational intelligence combines various nature-inspired computational methodologies and draws on rigorous quantitative approaches across computer science, mathematics, physics, and life sciences. Some of its research topics, such as artificial neural networks, fuzzy logic, evolutionary computation, and swarm intelligence, are traditional to computational intelligence. Other areas have established their relevance to the field fairly recently: embodied intelligence (Pfeifer and Bongard, 2006; Der, 2014), information theory of cognitive systems (Lungarella and Sporns, 2006; Polani et al., 2007; Ay et al., 2008), guided self-organization (Prokopenko, 2009; Der and Martius, 2012), and evolutionary game theory (Vincent and Brown, 2005). The intelligence phenomenon continues to fascinate scientists and engineers, remaining an elusive moving target. Following numerous past observations (e.g., Hofstadter, 1985, p. 585), it can be pointed out that several attempts to construct “artificial intelligence” have turned to designing programs with discriminative power. These programs would allow computers to discern between meaningful and meaningless in similar ways to how humans perform this task. Interestingly, as noted by de Looze (2006) among others, such discrimination is based on etymology of “intellect” derived from Latin “intellego” (inter-lego): to choose between, or to perceive/read (a core message) between (alternatives). In terms of computational intelligence, the ability to read between the lines, extracting some new essence, corresponds to mechanisms capable of generating computational novelty and choice, coupled with active perception, learning, prediction, and post-diction. When a robot demonstrates a stable control in presence of a priori unknown environmental perturbations, it exhibits intelligence. When a software agent generates and learns new behaviors in a self-organizing rather than a predefined way, it seems to be curiosity-driven. When an algorithm rapidly solves a hard computational problem, by efficiently exploring its search-space, it appears intelligent. In short, innovation and creativity shown within a rich space shaped by diverse, “entropic” forces, appeal to us as cognitive traits (Wissner-Gross and Freer, 2013). Can this intuition be formalized within rigorous and generic computational frameworks? What are the crucial obstacles on such a path? Intuitively, intelligent behavior is expected to be predictable and stable, but sensitive to change. Attempts to formalize this duality date back at least to cybernetics. For example, Ashby’s well-known Law of Requisite Variety states that an active controller requires as much variety (number of states) as that of the controlled system to be stable (Ashby, 1956). In order to explain the generation of behavior and learning in machines and living systems, Ashby also linked the concepts of ultrastability and homeostatic adaptation (Di Paolo, 2000; Fernandez et al., 2014). The balance between robustness and adaptivity is often attained near “the edge of chaos” (Langton, 1990), and the corresponding phase transitions are typically detected via high sensitivities to underlying control parameters (thermodynamic variables) (Prokopenko et al., 2011). Stability in self-organizing systems can be generally related to negentropy, the entropy that the system exports (dissipates) to keep its own entropy low (Schrodinger, 1944). Despite significant advances in this direction, the fundamental question whether stability, within processes developing far from an equilibrium, necessitates specific entropy dynamics is still unanswered. Clarifying the connections between entropy dynamics and stable but adaptive behavior is one of the grand challenges for computational intelligence. Put simply, we need to know whether learning and self-organization necessitate phase transitions in certain spaces, in terms of some order parameters. Is it possible to characterize the richness of self-generated choice, intrinsic to intelligent behavior, with respect to generic thermodynamic principles? The notion of generating and actively exploiting new behaviors, which adequately match the environment highlights that to be intelligent is to be complex in creating innovations. And so a mechanism producing computational novelty needs to exceed some threshold of complexity. To be truly impressive in generating endogenous innovation, it needs to be capable of universal computation, or to approach this capability in finite implementations (Casti, 1994; Markose, 2004). In other words, computational novelty may be fundamentally related to undecidability. Again, serious advances have been made in this foundational area of computer science. For example, Casti (1991) analyzed deeper interconnections between dynamical systems, Turing Machines, and formal logic systems: in particular, the complex, class IV, cellular automata were related to formal systems with undecidable statements (Godel’s incompleteness theorem) and the Halting Problem. Nevertheless, the question whether universal computation is the ultimate innovation-generator is still unresolved, offering another grand challenge: how computational intelligence, including mechanisms producing richness of choice and novelty, is related to

Control of Artificial Swarms with DDDAS

A framework for incorporating a swarm intelligent system with the Dynamic Data Driven Application System (DDDAS) is presented. Swarm intelligent systems, or artificial swarms, self-organize into useful emergent structures that are capable of solving complex problems, but are difficult to control and predict. The DDDAS concept utilizes repeated simulations of an executing application to improve analytic and predictive capability by creating a synergistic feedback loop. Incorporating DDDAS with swarm applications can significantly improve control of the swarm. An overview of the DDDAS framework for swarm control is presented, and then demonstrated with an example swarm application.

Elasticity-Based Mechanism for the Collective Motion of Self-Propelled Particles with Springlike Interactions: A Model System for Natural and Artificial Swarms

We introduce an elasticity-based mechanism that drives active particles to self-organize by cascading self-propulsion energy towards lower-energy modes. We illustrate it on a simple model of self-propelled agents linked by linear springs that reach a collectively rotating or translating state without requiring aligning interactions. We develop an active elastic sheet theory, complementary to the prevailing active fluid theories, and find analytical stability conditions for the ordered state. Given its ubiquity, this mechanism could play a relevant role in various natural and artificial swarms.

Artificial glowworm swarm optimization algorithm based on adaptive t distribution mixed mutation

The convergence speed of Artificial Glowworm Swarm Optimization(AGSO) algorithm declines,even falls into local minimums,when some glowworms gather in non whole extreme points or some glowworms wander around aimlessly.Concerning this problem,an AGSO algorithm based on adaptive t distribution mixed mutation was proposed.Adaptive t distribution mutation and optimization adjustment mutation was introduced into the AGSO algorithm to improve the diversity of glowworm swarm,and prevent the AGSO algorithm from falling into local minimums.Mutation control factor was defined.Combining history status information,the description of adaptive t distribution mixed mutation was given.The mutation method could enhance ability of global exploration and local development.The emulation results of representative test functions and many application examples show that the proposed algorithm is reliable and efficient.Meanwhile,this algorithm is better than tradition algorithm in terms of speed and precision for seeking the optimum.

Neural Network Assisted Particle Swarm Optimization of Machining Process

In this paper, optimization system based on the artificial neural networks (ANN) and particle swarm optimization (PSO) algorithm was developed for the optimization of machining parameters for turning operation. The optimization system integrates the neural network modeling of the objective function and particle swarm optimization of turning parameters. New neural network assisted PSO algorithm is explained in detail. An objective function based on maximum profit, minimum costs and maximum cutting quality in turning operation has been used. This paper also exhibits the efficiency of the proposed optimization over the genetic algorithms (GA), ant colony optimization (ACO) and simulated annealing (SA).

Collective motion dynamics of active solids and active crystals

We introduce a simple model of self-propelled particles connected by linear springs that describes a semi-rigid formation of active agents without explicit alignment rules. The model displays a discontinuous transition at a critical noise level, below which the group self-organizes into a collectively translating or rotating state. We identify a novel elasticity-based mechanism that cascades self-propulsion energy towards lower-energy modes as responsible for such collective motion and illustrate it by computing the spectral decomposition of the elastic energy. We study the model's convergence dynamics as a function of system size and derive analytical stability conditions for the translating state in a continuous elastic sheet approximation. We explore the dynamics of a ring-shaped configuration and of local angular perturbations of an aligned state. We show that the elasticity-based mechanism achieves collective motion even in cases with heterogeneous self-propulsion speeds. Given its robustness, simplicity and ubiquity, this mechanism could play a relevant role in various biological and artificial swarms.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

Based on Artificial Bee Colony Video Feature Classification of Video Watermarking Algorithm

This paper according to the video features using the method of evolution of video image classification processing, suitable for video performance and human visual characteristics of video watermarking, and put forward a kind of using artificial swarm evolution algorithm for video image classification processing of video watermarking algorithm, improve the watermark performance.

A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.

Towards swarm calculus: urn models of collective decisions and universal properties of swarm performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a ‘swarm calculus’ that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive-feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm’s current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported.