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In this paper, an improved multi-objective ABC algorithm based on k-means clustering, called CMOABC, is proposed. For keeping the population diversity, the multi-swarm technology based on k-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iterations, the population will be re-clustered to facilitate information exchange among different clusters. CMOABC is applied to solve the real-world Optimal Power Flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

This work considered the utilization of biomimicry of bacterial foraging strategy to develop an adaptive control strategy for mobile robot, and proposed a bacterial foraging approach for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation studies, a test scenario of static environment with different number obstacles is adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.

This paper presents a novel optimization algorithm, namely hierarchical artificial bee colony optimization (HABC), which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to cooperative and hierarchical fashion. The higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Furthermore, HABC is applied in predicting the droplet characteristic based on lumped element modeling methods. The simulation results demonstrate that the effectiveness of the proposed method.

Firefly Algorithm (FA) is a powerful swarm intelligence algorithm inspired by the flash phenomenon of the fireflies. However, it has weaknesses on optimizing high-dimensional problems. This paper presents an improved FA named Adaptive Firefly Algorithm (AFA). In the new algorithm, three strategies are proposed to improve its adaptability and overcome its weaknesses. The algorithm is tested on a set of benchmark functions with different dimensions. Canonical FA and two other well-known heuristic algorithms are also employed for comparison. Experiment and statistical results show that the AFA algorithm offers significant better performance than original FA algorithm. Especially on high-dimensional problems, it is superior to all other compared algorithms.

The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another's operation. Such reader collision must be minimized to avoid the faulty or miss reads. This paper, therefore, aims to use a successful swarm intelligence technique called artificial bee colony (ABC) algorithm to minimize both the reader-to reader interference and total system transaction time in RFID reader networks. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of artificial bee colony (BABC) algorithm is proposed in this study. Unlike the original ABC algorithm, the proposed BABC represents a food source as a discrete binary variable and applies discrete operators to change the foraging trajectories of the employed bees, onlookers and scouts in the probability that a coordinate will take on a zero or one value. Numerical results for two test cases with different scales, which contain 30 and 60 readers respectively, have been presented to demonstrate the performance of the proposed methodology.

Plasmon-enhanced light absorption in a multicomponent Ag/Ag2O/TiO2/N3 dye-sensitized solar cell (DSSC) core–shell nanostructure is studied using a hybrid quantum mechanics/classical electrodynamics (QM/ED) methodology in which the Ag/Ag2O/TiO2 nanostructure is treated by the finite-difference time-domain method and the N3 dye is treated by real-time time-dependent density functional theory. As part of this modeling, the undetermined thickness of the nonplasmonic Ag2O layer on the Ag/Ag2O/TiO2 particle was estimated by comparing the computed plasmon wavelength with experimental results. Also, absorption cross sections for the N3 dye were calculated for different locations of the dye on the TiO2 surface. The spatially averaged absorption cross sections for different thicknesses of TiO2 were evaluated and used to estimate the relative incident photon conversion efficiency. Encouragingly, it is found that the QM/ED calculations can well reproduce the factor of ∼10 experimental extinction difference spectrum a...

A simple yet reliable valence bond theory was applied to ascertain the effective size and shape for the electron and hole polarons in bulk anatase TiO2 by examining the extent of polaron charge delocalization. It was found that the electron polaron is approximately 2 times as large as its hole counterpart, leading to a faster electron diffusion than hole hopping with regard to the electron–phonon coupling strength. Moreover, the oblate hole polaron exhibits a pronounced directional heterogeneity in migration, whereas the nearly spherical electron polaron tends to diffuse along all possible lattice directions. In light of the notable delocalization characteristics of both polarons, their migration should proceed in an adiabatic manner, and their rates can be calculated by the Arrhenius equation. It turns out that our calculated polaron mobilities at 300 and 1300K are both in excellent agreement with experimental values, justifying our novel approach for Holstein polaron modeling in crystalline semiconductors.

In conjunction with the constrained density functional theory, a valence-bond representation has been employed to model the migration of anionic polaron in bulk rutile TiO2. It was found that the charge delocalization of a self-trapped electron proceeded predominately along the c crystal axis of rutile, thus exhibiting pronounced directional heterogeneity of polaron migration. As a result, the extrapolated polaron activation energies are 0.026 eV and 0.195 eV along the [001] and [111] lattice vectors, respectively. According to the Holstein theory, the difference on the activation energy makes the polaron drift over 100 times faster along the c crystal axis than on the ab crystal plane at room temperature. The notable anisotropy of the anionic polaron was also reflected through the electron paramagnetic resonance (EPR) g-matrix, whose principal component along [001] is substantially smaller than that along [110] or [11̅0]. Finally, the extent of polaron charge was probed by our calculated isotropic hyperfine coupling constants on two groups of crystallographically inequivalent (17)O atoms, which manifest distinct strengths of spin-orbit interaction with the unpaired electron.

A novel approach based on constrained real-time time-dependent density functional theory (C-RT-TDDFT) is introduced to accurately evaluate the electronic Hamiltonian coupling associated with photoinduced electron transfer (PIET) using diabatic states that are defined using constrained DFT (C-DFT). In combination with the semiclassical Marcus theory, the photoexcited ET rate for coherently coupled photoexcitation and electron transfer is determined for a given incident wavelength by combining this Hamiltonian coupling with free energy changes and ground state reorganization energies that are obtained using an implicit solvation model. As an application of this method, we consider PIET for the (Ag20–Ag)+ complex as a model of a plasmon-enhanced electron transfer process. Using solar radiation intensity, the fastest PIET rate is found to be induced by an incident wavelength that is distinct (blue-shifted) from the wavelength of strongest plasmon-like excitation associated with the Ag20+ cluster, particularly...

A theoretical calculation was performed to evaluate the photo-induced electron transfer (PIET) rate between a gold atom and a gold ion solvated in carbon tetrachloride (CCl4) in the framework of Marcus electron transfer (ET) theory, including both solvent reorganization effects and electronic wavefunction coupling between the ET diabatic states. A novel component of this work involves calculation of the electronic coupling strength using a recently developed constrained real-time time-dependent density-functional-theory (CRT-TDDFT) method. It is found that the PIET rate reaches its maximum value at the electronic resonance wavelength regardless of the inter-particle separation, suggesting a strong correlation between PIET and light absorption. In comparison with thermally activated electron transfer (TAET) at room temperature, light irradiation is demonstrated to be much more efficient than thermal fluctuations in promoting long-range ET, at least for the most common situations, when the light travelling substantially exceeds thermal energy. This work is the first step towards a quantum theory of plasmon enhanced electron transfer, and the theory can also be used to calculate electron transfer rates quite generally for condensed phase problems.

In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel multi-swarm particle swarm optimizer called PS^2O. The main idea of PS^2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. This algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With five mathematical benchmark functions, PS^2O is proved to have significantly better performance than five successful variants of PSO. PS^2O is then used for solving the real-world RFID network planning problem. Simulation results show that the proposed algorithm proves to be superior for planning RFID networks than canonical PSO, multi-swarm cooperative PSO (MCPSO), and two evolutionary algorithms, namely genetic algorithm with elitism (EGA) and self-adaptive evolution strategies (SA-ES), in terms of optimization accuracy and computation robustness.

Prevention of cation permeation in wild-type aquaporin-1 (AQP1) is believed to be associated with the Asn-Pro-Ala (NPA) region and the aromatic/arginine selectivity filter (SF) domain. Previous work has suggested that the NPA region helps to impede proton permeation due to the protein backbone collective macrodipoles that create an environment favoring a directionally discontinuous channel hydrogen-bonded water chain and a large electrostatic barrier. The SF domain contributes to the proton permeation barrier by a spatial restriction mechanism and direct electrostatic interactions. To further explore these various effects, the free-energy barriers and the maximum cation conductance for the permeation of various cations through the AQP1-R195V and AQP1-R195S mutants are predicted computationally. The cations studied included the hydrated excess proton that utilizes the Grotthuss shuttling mechanism, a model "classical" charge localized hydronium cation that exhibits no Grotthuss shuttling, and a sodium cation. The hydrated excess proton was simulated using a specialized multi-state molecular dynamics method including a proper physical treatment of the proton shuttling and charge defect delocalization. Both AQP1 mutants exhibit a surprising cooperative effect leading to a reduction in the free-energy barrier for proton permeation around the NPA region due to altered water configurations in the SF region, with AQP1-R195S having a higher conductance than AQP1-R195V. The theoretical predictions are experimentally confirmed in wild-type AQP1 and the mutants expressed in Xenopus oocytes. The combined results suggest that the SF domain is a specialized structure that has evolved to impede proton permeation in aquaporins.

In this work, we have combined constant-pH molecular dynamics simulations and experiments to provide a quantitative analysis of pH dependent interactions between doxorubicin hydrochloride (DOX) cancer therapeutic and faceted nanodiamond (ND) nanoparticle carriers. Our study suggests that when a mixture of faceted ND and DOX is dissolved in a solvent, the pH of this solvent plays a controlling role in the adsorption of DOX molecules on the ND. We find that the binding of DOX molecules on ND occurs only at high pH and requires at least ∼10% of ND surface area to be fully titrated for binding to occur. As such, this study reveals important mechanistic insight underlying an ND-based pH-controlled therapeutic platform.

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior ofE. colibacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

Nonbonded interactions between molecules usually include the van der Waals force and computationally expensive long-range electrostatic interactions. This article develops a more efficient approach: the effective-interaction multistate empirical-valence-bond (EI-MS-EVB) model. The EI-MS-EVB method relies on a mapping of all interactions onto a short-range and thus, computationally efficient effective potential. The effective potential is tabulated by matching its force to known trajectories obtained from the full-potential empirical multistate empirical-valence-bond (MS-EVB) model. The effective pairwise interaction depends on and is uniquely determined by the atomic configuration of the system, varying only with respect to the hydrogen-bonding topology. By comparing the EI-MS-EVB and full MS-EVB calculations of several equilibrium and dynamic properties important to hydrated excess proton solvation and transport, we show that the EI-MS-EVB model produces very accurate results for the specific system in which the tabulated potentials were generated. The EI-MS-EVB potential also transfers reasonably well to similar systems with different temperatures and box sizes. The EI-MS-EVB method also reduces the computational cost of the nonbonded interactions by about 1 order of magnitude in comparison with the full algorithm.

A new multiscale computational methodology was developed to effectively incorporate the scattered electric field of a plasmonic nanoparticle into a quantum mechanical (QM) optical property calculation for a nearby dye molecule. For a given location of the dye molecule with respect to the nanoparticle, a frequency-dependent scattering response function was first determined by the classical electrodynamics (ED) finite-difference time-domain (FDTD) approach. Subsequently, the time-dependent scattered electric field at the dye molecule was calculated using the FDTD scattering response function through a multidimensional Fourier transform to reflect the effect of polarization of the nanoparticle on the local field at the molecule. Finally, a real-time time-dependent density function theory (RT-TDDFT) approach was employed to obtain a desired optical property (such as absorption cross section) of the dye molecule in the presence of the nanoparticle’s scattered electric field. Our hybrid QM/ED methodology was de...

In order to obtain accurate and reliable network planning in the Radio Frequency Identification (RFID) communication system, the locations of readers and the associated values for each of the reader parameters have to be determined. All these choices must optimize a set of objectives, such as tag coverage, economic efficiency, load balance, and interference level between readers. This paper proposes a novel optimization algorithm, namely the multi-colony bacteria foraging optimization (MC-BFO), to solve complex RFID network planning problem. The main idea of MC-BFO is to extend the single population bacterial foraging algorithm to the interacting multi-colony model by relating the chemotactic behavior of single bacterial cell to the cell-to-cell communication of bacterial community. With this multi-colony cooperative approach, a suitable diversity in the whole bacterial community can be maintained. At the same time, the cell-to-cell communication mechanism significantly speeds up the bacterial community to converge to the global optimum. Then a mathematical model for planning RFID networks is developed based on the proposed MC-BFO. The performance of MC-BFO is compared to both GA and PSO on RFID network planning problem, demonstrating its superiority.

Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge to the global optimum. The PS2O algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), PS2O is proved to have significantly better performance than four other successful variants of PSO.

Hydrophobic interaction, which is believed to be a primary driving force for many fundamental chemical and biological processes such as nanostructure self-assembly, micelle formation, and protein folding, is different in acidic aqueous solutions compared to salt solutions. In this study, the aggregation/dispersion behavior of nonpolar hydrophobic molecules in aqueous solutions with varying acid (HCl) concentrations is investigated using novel molecular dynamics simulations and compared to the hydrophobic behavior in corresponding salt (NaCl) solutions. The formation of unusual weakly bound hydrophobe-hydrated proton solvation structures is observed and can be attributed to the unique "amphiphilic" characteristic of hydrated protons. This molecular-level mechanism for the acid-enhanced dissolution of hydrophobic particles also provides a novel interpretation for the apparent anomaly of the hydronium cation in the Hofmeister series.

A new multistate empirical valence bond model (MS-EVB3) is developed for proton solvation and transport in aqueous solutions. The new model and its quantum version (qMS-EVB3) are based on the MS-EVB2 model [Day et al., J. Chem. Phys. 2002, 117, 5839] and recently developed flexible water models-the SPC/Fw model [Wu et al. J. Chem. Phys. 2006, 124, 24503] and the qSPC/Fw model [Paesani et al. J. Chem. Phys. 2006, 125, 184507]-for classical and quantum simulations, respectively. Using ab initio data as benchmarks, the binding energies and optimized geometries calculated with the new model for protonated water clusters, as well as the potential energy surface for proton shuttling between water molecules in a cluster environment, are improved in comparison to the MS-EVB2 model. For aqueous solutions, classical and quantum molecular dynamics simulations with the MS-EVB3 model yield a more accurate description of the solvation structure and diffusive dynamics of the excess proton. New insight is also provided into the proton solvation and hopping dynamics in water, as well as the "amphiphilic" nature of the hydrated proton that has been predicted to give rise to its enhanced concentration at aqueous interfaces and an effectively lower pH of the air-water interface [Petersen et al. J. Phys. Chem. B 2004, 108, 14804].

The structural properties of the influenza A virus M2 transmembrane channel in dimyristoylphosphatidylcholine bilayer for each of the four protonation states of the proton-gating His-37 tetrad and their effects on proton transport for this low-pH activated, highly proton-selective channel are studied by classical molecular dynamics with the multistate empirical valence-bond (MS-EVB) methodology. The excess proton permeation free energy profile and maximum ion conductance calculated from the MS-EVB simulation data combined with the Poisson-Nernst-Planck theory indicates that the triply protonated His-37 state is the most likely open state via a significant side-chain conformational change of the His-37 tetrad. This proposed open state of M2 has a calculated proton permeation free energy barrier of 7kcal/mol and a maximum conductance of 53pS compared to the experimental value of 6pS. By contrast, the maximum conductance for Na+ is calculated to be four orders of magnitude lower, in reasonable agreement with the experimentally observed proton selectivity. The pH value to activate the channel opening is estimated to be 5.5 from dielectric continuum theory, which is also consistent with experimental results. This study further reveals that the Ala-29 residue region is the primary binding site for the antiflu drug amantadine (AMT), probably because that domain is relatively spacious and hydrophobic. The presence of AMT is calculated to reduce the proton conductance by 99.8% due to a significant dehydration penalty of the excess proton in the vicinity of the channel-bound AMT.

The explicit contribution to the free energy barrier and proton conductance from the delocalized nature of the excess proton is examined in aquaporin channels using an accurate all-atom molecular dynamics computer simulation model. In particular, the channel permeation free energy profiles are calculated and compared for both a delocalized (fully Grotthuss shuttling) proton and a classical (nonshuttling) hydronium ion along two aquaporin channels, Aqp1 and GlpF. To elucidate the effects of the bipolar field thought to arise from two α-helical macrodipoles on proton blockage, free energy profiles were also calculated for computational mutants of the two channels where the bipolar field was eliminated by artificially discharging the backbone atoms. Comparison of the free energy profiles between the proton and hydronium cases indicates that the magnitude of the free energy barrier and position of the barrier peak for the fully delocalized and shuttling proton are somewhat different from the case of the (localized) classical hydronium. The proton conductance through the two aquaporin channels is also estimated using Poisson-Nernst-Planck theory for both the Grotthuss shuttling excess proton and the classical hydronium cation.

This paper proposed a novel bio-inspired optimizer, namely the root system growth algorithm (RSGA), which adopts the root foraging, memory and communication, and auxin-regulated mechanisms of the root system. When tested against benchmark functions, the RSGA markedly outperforms the CMA-ES, PSO, GA, and DE algorithms in terms of accuracy, robustness and convergence speed.

Through the observation and study of social creatures, biologists and computing experts proposed many bio-inspired optimization algorithm based on simulated biological behavior. These algorithms show strong vitality and potential of further development in solving traditional NP and practical complex optimization problem. But most existing research results mainly focused on the use of biological behavior phenotypic mechanism to achieve the function optimization and engineering control, but the analysis of internal control mechanisms of biological behavior is not sufficient, it needs further research. Based on complex system dynamics model, this paper proposes a bio-inspired dynamics optimization decision method. This method combines the swarm dynamics model, population logistic growth model and three evolve elements. Based on the test of constrained optimization problems, experiment results indicate that this method is effective. Finally, conclusion and the further work are given.

Multilevel thresholding is of great importance in the fields of image segmentation techniques. This paper proposes multilevel thresholding for color image segmentation using cooperative bacterial foraging algorithm (CBFA), which combined bacterial chemo taxis, cell-to-cell communication, and an adaptive foraging mechanism to extend original bacterial foraging algorithm. The superiority of the proposed algorithm can reach a higher quality adequate segmentation and reduce the CPU processing time, which is demonstrated by comparing with other algorithm.

Robust Principal Component Analysis is generalized to a multi-objective optimization problem, named as Multi-objective Robust Principal Component Analysis (MRPCA) in this paper. We aim to solve MRPCA via Evolutionary Algorithm. To the best knowledge of authors, this is the first attempt to use evolutionary algorithm to solve MRPCA problem, which is a high dimension convex optimization problem. Specifically, one of the popular evolutionary algorithm, NSGA-II, is tested on MRPCA problem. The curse of dimensionality is observed when the dimension of MRPCA problem increases. Since this problem is convex, which is a friendly structure, we propose a modified NSGA-II by introducing a new mutation method: ADMM (Alternating Direction Method of Multipliers) mutation. Numerical experiments show our modified NSGA-II algorithm converges much faster than the standard one.

The major challenge in printable electronics fabrication is to effectively and accurately control a drop-on-demand DoD inkjet printhead for high printing quality. In this paper, a prediction model based on Lumped Element Modeling LEM is proposed to search the parameters of driving waveform for obtaining the desired droplet properties. Although the evolution algorithms are helpful to solve this problem, the classical evolution algorithms may get trapped into local optimal due to the inefficiency of local search. To overcome it, we present an improved artificial bee colony algorithm based on K-means clustering KCABC, which enhances the population diversity by dynamically clustering and increases the convergence rates by the modification of information communication in the employed bees' phase. Combined with KCABC, the prediction model is applied to optimize the droplet volume and velocity of nano-silver ink for high printing quality. Experimental results demonstrate the proposed prediction model with KCABC plays a good performance in terms of efficiency and accuracy of searching the appropriate combination of waveform parameters for printable electronics fabrication.

This chapter presents a novel multilevel threshold approach based on improved particle swarm optimization called as hybrid particle swarm optimization (HPSO), which can combine the advantages of the particle swarm optimization and the neighbor searching of artificial bee colony algorithm for color image segmentation. Experimental results show that the proposed approach adopts the multilevel threshold technique based on the improved HPSO algorithm to obtain a higher quality adequate segmentation; at the same time, it can also reduce the CPU processing time and eliminate the particles falling into local minima.

An improved multiobjective ABC algorithm based onK-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based onK-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II), the multiobjective particle swarm optimizer (MOPSO), and the multiobjective ABC (MOABC). Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF) problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

The simulations on equilibrium thermodynamic properties of short homopolymer chains, 3-, 4-, and 5-mer, by multicanonical Monte Carlo in a three-dimensional off-lattice model are quantitatively coincident with the exact analytical results of recently reported by Taylor [J. Chem. Phys. 114, 6472 (2001)], indicating that multicanonical algorithm is a permitted method in overcoming nonergodicity in observation of freezing transitions of single macromolecular chains. The investigation of freezing transition behaviors of 5-mer chains at ultralow temperatures reveals that four isomers coexist in the liquid state, then freeze into one of four isomers in the solid state depending on the bond length and bond fluctuation of the chain model after the occurrence of a liquid-to-solid-like first-order transition.

In the process of material composition detection, the image analysis is an inevitable problem. Multilevel thresholding based OTSU method is one of the most popular image segmentation techniques. How, with the increase of the number of thresholds, the computing time increases exponentially. To overcome this problem, this paper proposed an artificial bee colony algorithm with a two-level topology. This improved artificial bee colony algorithm can quickly find out the suitable thresholds and nearly no trap into local optimal. The test results confirm it good performance.

Nanoscale electronic transport is of intense technological interest, with applications ranging from semiconducting devices and molecular junctions to charge migration in biological systems. Most explicit theoretical approaches treat transport using a combination of density functional theory (DFT) and nonequilibrium Green’s functions. This is a static formalism, with dynamic response properties accommodated only through complicated extensions. To circumvent this limitation, the carrier density may be propagated using real-time time-dependent DFT (RT-TDDFT), with boundary conditions corresponding to an open quantum system. Complex absorbing potentials can emulate outgoing particles at the simulation boundary, although these do not account for

A solid approach has been developed to ascertain the correlation of electron transfer with molecular vibration in a quantitative manner. Specifically, the reaction coordinate is identified by maximizing the linear Pearson's correlation coefficient between atomic displacement and the diabatic energy gap. In the limit of fast molecular vibration, the rates of electron transfer driven by multiple vibrational modes have been derived respectively under the strong and weak vibronic coupling conditions. Our functional mode electron-transfer theory is then justified by investigating the electron transfer of a betaine-30 molecule from its first excited state to its ground state when being solvated in glycerol triacetate. Among the 210 available vibrational modes of betaine-30, only seven are essential to the electron transfer by cumulatively accounting for more than 60% of the total reorganization energy. Because all essential vibrational modes are significantly faster than thermal fluctuation, the electron transfer is primarily driven by intramolecular quantum tunneling. Interestingly, the calculated reaction driving force of 1.95 eV is substantially greater than the reorganization energy of 0.58 eV, placing the reaction in the inverted Marcus region. Nevertheless, a sizable Franck-Condon factor of 1.58 × 10(-3) eV(-1) is still achieved due to the large vibronically weighted zero-point energy of the essential vibrational modes. After determining the electronic coupling strength as 0.14 eV by the constrained density functional theory, the overall electron-transfer rate at 300 K is found to be 0.30 ps(-1), which agrees nearly perfectly with experimental values.

The development of radio frequency identification (RFID) technology generates the most challenging RFID network planning (RNP) problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA) and swarm intelligence (SI) for solving multiobjective RNP (MORNP) has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC), the nondominated sorting genetic algorithm II (NSGA-II), and the multiobjective particle swarm optimization (MOPSO), on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

Image segmentation is of great importance in the fields of computer vision, face recognition, medical imaging, digital libraries, and video retrieval. This paper presents a novel method for image segmentation based on a Hybrid particle swarm algorithm, which combines the advantages of swarm intelligence and the natural selection mechanism of artificial bee colony algorithm. Experimental results show that the proposed method can reach a higher quality adequate segmentation, reduce the CPU processing time and eliminate the particles falling into local minima.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO). The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem.

This work proposes a novel hot routes inferring approach without the support of real road network. Discovery of hot routes is important to the applications that requiring classifies the traffic flow or profiles the dynamic of the city, such as targeted advertising, traffic management and control. The advances of location-acquisition technologies have led to a huge collection of objects’ trajectories in the road network, which give us the chances to finding hot routes conveniently. However, it is difficult to effectively detect hot routes without the support of the available road map. To address this issue, we first develop a Road Network Constructing Algorithm (RNCA) that extract road network from vehicular trajectories using image processing methods, and then propose a Hot Route Inferring Algorithm (HRRA) based on the extracted road network. Meanwhile, a novel road matching operation is also developed to match trajectory points onto roads. We have conducted extensive experiments on real dataset of taxi trajectories. Simulation results show that the proposed RNCA and HRRA are both effective and efficient.

Three kinds of UV-curable prepolymers, urethane acrylate (UA), polysilicone acrylate (SA), and epoxy acrylate (EA) were prepared. These prepolymers were mixed in different proportions to modify the properties of an optical fiber coating. The relationships of component-property and structure-property of the mixture coatings were studied. When the proportion was SA:UA:EA=5:4:3, it was used for a single coating, however, the proportion SA:UA:EA=6:5:2 was used for a primary buffer coating. These mixture coatings were applied to optical fibers.

Most bio-inspired algorithms simulate the behaviors of animals. This paper proposes a new plant-inspired algorithm named Root Mass Optimization (RMO). RMO simulates the root growth behavior of plants. Seven well-known benchmark functions are used to validate its optimization effect. We compared RMO with other existing animal-inspired algorithms, including artificial bee colony (ABC) and particle swarm optimization (PSO). The experimental results show that RMO outperforms other algorithms on most benchmark functions. RMO provides a new reference for solving optimization problems.

Bio-inspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biology life cycle, this paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization. LSO algorithm simulates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. Experiments were conducted on 7 unimodal functions. The results demonstrate remarkable performance of the LSO algorithm on those functions when compared to several successful optimization techniques.