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Real life networks heavily rely on higher-order interactions. This paper studies the diffusibility of novel quasi-star higher-order networks from two aspects. When the coupling strengths of the lower-order and higher-order coupling parts are less than 1, the diffusibility can be maximized by choosing the intermediate values of these coupling strengths. When they are far greater than 1, there exists an inferior limit of the diffusibility, and the lower-order coupling part has more impact on quasi-star network's diffusibility. Additionally, the face with the middle order in the higher-order coupling part plays a more important role in enhancing the network's diffusibility compared with other faces, and the diffusibility does not naturally increase with the order of face. These results give us some insights into the influence of higher-order structures and their faces. • Higher-order interactions are generally found in brain network. Functions of brain are significantly affected by remote synchronization, which is popular in quasi-star coupling structure. To depict this type of topology, a higher-order quasi-star network is investigated. • To our knowledge, the diffusibility of higher-order networks has never been investigated before from the topology of a network. We focus on this topic and obtain some interesting results. • When the coupling strengths of the lower-order and higher-order coupling parts are less than 1, the diffusibility can be maximized by choosing the intermediate values of these coupling strengths. • When the coupling strengths of the lower-order and higher-order coupling parts are far greater than 1, the lower-order coupling part has more impact on quasi-star network's diffusibility. • We further extend the higher-order coupling parts to simplexes with various orders, and discover some interesting phenomena. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to introduce a new NIO algorithm inspired from the hunting strategy of the leopard seals called Leopard Seal Optimization (LSO)to provide a simple swarm intelligence algorithm that have a high flexibility to solve real-time engineering problems in a fast and more accurate manner without falling into local optima problem. LSO is compared against recent NIO algorithms considering feature selection for disease diagnosing as the underlying optimization problem. In experimental results, LSO has been statistically tested against a recent six swarm intelligence algorithms using Wilcoxon test and t-test with significance level equals 0.05 based on the common five benchmark functions. These recent algorithms are called Tuna Swarm Optimization (TSO), Pelican Optimization Algorithm (POA), Cat and Mouse-Based Optimization (CMBO), Aphid–Ant Mutualism (AAM), White Shark Optimizer (WSO), and Red Piranha Optimization (RPO). The statistical analysis proved that LSO outperforms other recent techniques in most cases where the most tested results are less than 0.05. Then, LSO has been tested against the same algorithms in binary version as feature selection algorithms using two metrics called accuracy and execution time. It is noted that LSO is faster and more accurate than other algorithms to determine the optimal set of features at all numbers of search agents. Finally, it is concluded that LSO outperforms other techniques using accuracy, execution time, Wilcoxon test, and t-test metrics. Compared to the recent techniques using the maximum iteration number=400, LSO provided the maximum accuracy value that equals 97.62% at the search agents number=120 and the minimum execution time that equals 1314 at the search agents number= 40. • A new NIO algorithm inspired from the hunting strategy of the leopard seals called Leopard Seal Optimization (LSO) is introduced to provide a simple swarm intelligence algorithm that have a high flexibility to solve real-time engineering problems in a fast and more accurate manner without falling into local optima problem. • LSO is compared against recent NIO algorithms considering feature selection for disease diagnosing as the underlying optimization problem. • In experimental results, LSO has been statistically tested against a recent six swarm intelligence algorithms using Wilcoxon test and t-test with significance level equals 0.05 based on the common five benchmark functions. • Then, LSO has been tested against the same algorithms in binary version as feature selection algorithms using two metrics called accuracy and execution time. • It is noted that LSO is faster and more accurate than other algorithms to determine the optimal set of features at all numbers of search agents. [ABSTRACT FROM AUTHOR]

This paper reveals the dynamical properties of a nonlinear vibration reduction system with time delay feedback control and quasi-zero stiffness structure. The stable and unstable regions of the trivial solution of the system in the parameter plane of time delay and feedback gain are given. The amplitude–frequency responses and force transmissibility of the system under harmonic excitation are determined. The optimized parameters are obtained by using genetic algorithm. The complicated oscillations are explored, such as chaotic motions and multi-periodic orbits. Afterwards, the displacement and the energy of the primary structure and the force transmitted to the base are discussed under the shock excitation and random excitation respectively. The influences of time delay on the dynamical behaviors and reduction performance are shown. • A nonlinear vibration reduction system with time delay feedback control and quasizero stiffness structure is presented. • The dynamical response and reduction performance of the nonlinear delayed system under different external excitations are given. • The effects of the time delay and control gain on the vibration reduction performance are shown. [ABSTRACT FROM AUTHOR]

• In this article we study the latest bio-inspired algorithms that emerged in at most a decade ago. • These algorithms are studied from the point of view of medical image segmentation based on multi-thresholding, which is a challenger with high computational time. • In our paper, the evaluation functions used in these algorithms are based on the non-extensive Tsallis entropy, which has been demonstrated its efficiency in several physical systems. However, for most of the algorithms studied here, the application of this type of entropy as an evaluation function had not yet been tried. Recently, many algorithms have emerged inspired by the biological behavior of animal life to deal with complicated applications such as combinatorial optimization. One of the most critical discussions involving these algorithms is concerning their objective functions. Also, recently, many works have demonstrated the efficiency of Tsallis non-extensive statistics in several applications. However, this formalism has not yet been tested in most recent bio-inspired algorithms as an evaluation function. Thus, this paper presents a study of seven of the most promising bio-inspired algorithms recently proposed (a maximum one decade), from this entropy applied to the multi-thresholding segmentation of medical images. The results show the range of values of q , the so-called non-extensivity parameter of the Tsallis entropy, for which the algorithms tested here have their best performance. It is also demonstrated that the Firefly algorithm (FFA) is the one that obtained the best performance in terms of segmentation, and Grey Wolf Optimizer (GWO) presents the fastest convergence. [ABSTRACT FROM AUTHOR]

In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results. [ABSTRACT FROM AUTHOR]

A goal of many inverse problems is to find unknown parameter values, λ ∈ Λ , so that the given observed data u true agrees well with the solution data produced using these parameters u λ . Unfortunately finding u λ in terms of the parameters of the problem may be a difficult or even impossible task. Further, the objective function may be a complicated function of the parameters λ ∈ Λ and may require complex minimization techniques. In recent literature, the collage coding approach to solving inverse problems has emerged. This approach avoids the aforementioned difficulties by bounding the approximation error above by a more readily minimizable distance, thus making the approximation error small. The first of these methods was applied to first-order ordinary differential equations and gets its name from the “collage theorem” used in this setting to achieve an upperbound on the approximation error. A number of related ODE problems have been solved using this method and extensions thereof. More recently, collage-based methods for solving linear and nonlinear elliptic partial differential equations have been developed. In this paper we establish a collage-based method for solving inverse problems for nonlinear hyperbolic PDEs. We develop the necessary background material, discuss the complications introduced by the presence of time-dependence, establish sufficient conditions for using the collage-based approach in this setting and present examples of the theory in practice. [ABSTRACT FROM AUTHOR]

This paper is concerned with a minimization problem modeling the minimum displacement of an isotropic elastic membrane subject to a vertical force such as a load distribution. In addition to proving existence and uniqueness of optimal solutions, we show that these solutions are monotone and stable, in a certain sense. The main mathematical tool used in the analysis is the tangent cones from convex analysis, which helps to derive the optimality condition. Our results are compatible with physical expectations. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we consider a differentiated Stackelberg model, when the leader firm engages in an R&D process that gives an endogenous cost-reducing innovation. The aim is to study the licensing of the cost-reduction by a two-part tariff. By using comparative static analysis, we conclude that the degree of the differentiation of the goods plays an important role in the results. We also do a direct comparison between our model and Cournot duopoly model. [Copyright &y& Elsevier]

Abstract: 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 activity, and (iii) random diffusion. For more precise modeling of the krill behavior, two adaptive 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 algorithm is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms. [Copyright &y& Elsevier]

Abstract: The revival of multi-objective optimization is mainly resulted from the recent development of multi-objective evolutionary optimization that allows the generation of the overall Pareto front. This paper presents an algorithm called HOGA (High-dimensional Objective Genetic Algorithm) for high-dimensional objective optimization on the basis of evolutionary computing. It adopts the principle of Shannon entropy to calculate the weight for each object since the well-known multi-objective evolutionary algorithms work poorly on the high-dimensional optimization problem. To further discuss the nonlinear dynamic property of HOGA, a martingale analysis approach is then employed; some mathematical derivations of the convergent theorems are obtained. The obtained results indicate that this new algorithm is indeed capable of achieving convergence and the suggested martingale analysis approach provides a new methodology for nonlinear dynamic analysis of evolutionary algorithms. [Copyright &y& Elsevier]

Abstract: In rotor dynamics, the bearing characteristics exerts a decisive influence on dynamics of the rotating shaft. The research and application experience have led to active magnetic bearings (AMBs), which allow for unique applications in rotating systems. The paper presents the investigations concerning optimization of the magnetic bearing construction. An active magnetic bearing operates as a radial, auxiliary damper, which cooperates with the long, flexible shaft line (aircraft industry applications) and modifies its dynamic properties. In the developed concept of AMBs for aviation purposes, a necessity of increasing its bearing load capacity and damping has occurred. The second important criterion is a weight reduction. This advanced problem leads to specific requirements on the design and materials for the AMB. To achieve these goals, some simulations have been performed. The experimental results are presented as well. [ABSTRACT FROM AUTHOR]

Abstract: A genetic algorithm (GA) is proposed for simultaneous power quality improvement, optimal placement and sizing of fixed capacitor banks in radial distribution networks with nonlinear loads and distributed generation (DG) imposing voltage–current harmonics. In distribution systems, nonlinear loads and DGs are often considered as harmonic sources. For optimizing capacitor placement and sizing in the distribution system, objective function includes the cost of power losses, energy losses and capacitor banks. At the same time, constraints include voltage limits, number/size of installed capacitors (at each bus) and the power quality limits of standard IEEE-519. In this study, new fitness function is used to solve the constrained optimization problem with discrete variables. Simulation results for two IEEE distorted networks (18-bus and 33-bus test systems) are presented and solutions of the proposed method are compared with those of previous methods described in the literature. The main contribution of this paper is computing the (near) global solution with a lower probability of getting stuck at a local optimum and weak dependency on initial conditions, while avoiding numerical problems in large systems. Results show that proposed method could be effectively used for optimal capacitor placement and sizing in distorted distribution systems. [Copyright &y& Elsevier]

Abstract: This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods. [Copyright &y& Elsevier]

Abstract: To estimate the ultimate bound and positively invariant set for a dynamical system is an important but quite challenging task in general. This paper attempts to investigate the ultimate bounds and positively invariant sets of the hyper-chaotic Lorenz–Stenflo (L–S) system, which is based on the optimization method and the comparison principle. A family of ellipsoidal bounds for all the positive parameters values a, b, c, dand a cylindrical bound for a >0, b >1, c >0, d >0 are derived. Numerical results show the effectiveness and advantage of our methods. [Copyright &y& Elsevier]

Abstract: This paper deals with the design of fractional PID controller for integer and fractional plants. A new analytic method is proposed, the developments are based on the expansion of the control loop signals as well as a chosen reference model input and output over a piecewise orthogonal functions, namely, Block pulse, Walsh and Haar wavelets. The generalized operational matrices of differentiation related to these bases which are fitting the Riemann–Liouville definition accurately are used to replace the fractional differential calculus by an algebraic one easier to solve. Thereafter, the controller tuning is elaborated simply with a matrix manipulation manner. At first, a least square is drawn to find only the controller gains, then a nonlinear function defined as a matrix norm is minimized to optimize the whole parameters. A variety of examples covering both integer and fractional systems and reference models are presented to show the validity of the technique. [Copyright &y& Elsevier]

Abstract: This study addresses the optimization of rational fraction approximations for the discrete-time calculation of fractional derivatives. The article starts by analyzing the standard techniques based on Taylor series and Padé expansions. In a second phase the paper re-evaluates the problem in an optimization perspective by tacking advantage of the flexibility of the genetic algorithms. [Copyright &y& Elsevier]

Abstract: This paper considers an international trade under Bertrand model with differentiated products and with unknown production costs. The home government imposes a specific import tariff per unit of imports from the foreign firm. We prove that this tariff is decreasing in the expected production costs of the foreign firm and increasing in the production costs of the home firm. Furthermore, it is increasing in the degree of product substitutability. We also show that an increase in the tariff results in both firms increasing their prices, an increase in both expected sales and expected profits for the home firm, and a decrease in both expected sales and expected profits for the foreign firm. [Copyright &y& Elsevier]

Abstract: This paper deals with the pole-placement type robust adaptive control of continuous linear systems in the presence of bounded noise and a common class of unmodeled dynamics with the use of multiple estimation schemes working in parallel. The multiestimation scheme consisting of the above set of various single estimation schemes is a tool used to minimize the plant identification error by building an estimate which is a convex combination of the estimates at all time. The weighting functions of the individual estimates are provided at each time by a suboptimization scheme for a quadratic loss function of a possibly filtered tracking error and/or control input. The robust stability of the overall adaptive scheme is ensured by an adaptation relative dead zone which takes into account the contribution of the unmodeled dynamics and bounded noise. The basic results are derived for two different estimation strategies which have either a shared regressor with the plant or individual regressors for the input contribution and its relevant time-derivatives. In this second case, the plant input is obtained through a similar convex combination rule as the one used for the estimators in the first approach. An extension of the basic strategies is also pointed out including a combined use of the suboptimization scheme with a supervisor of past measures for the on-line calculation of the estimator weights in the convex combination. [Copyright &y& Elsevier]

This paper falls within the scope of studies concerning the optimization of the functioning of hydrothermal systems. We have developed a much simpler theory than previous ones that resolves the problem of minimization of a functionalF(z)=∫0TL(t,z(t),z′(t)) dtwithin the set of piecewise C1 functions (C1) that satisfy z(0)=0, z(T)=b and the constraints0⩽H(t,z(t),z′(t))⩽Pd(t) ∀t∈[0,T]In particular, we have established a necessary condition for the stationary functions of the functional. This theorem allows us to elaborate the optimization algorithm that leads to determination of the optimal functioning of the hydroplant and of the whole hydrothermal system. Finally, we present a example employing the algorithm realized to this end with the “Mathematica” package. The program developed is very simple and easy to use. [Copyright &y& Elsevier]

• Gradient based multi-material optimization. • Nonlinear transient unsaturated flow problem based on Richards' equation. • Optimized design for simplified diaper (no swelling). • Increase absorbed fluid up to 45%. We present a mathematical approach to optimize the material distribution for fluid transport in unsaturated porous media. Our benchmark problem is a simplified diaper model as an exemplary liquid absorber. Our model has up to three materials with vastly different properties, which in the reference configuration are arranged in parallel layers. Here, swelling is neglected and the geometry of a swollen diaper is assumed and treated as a porous medium of high porosity. The imbibition process is then simulated by solving an unsaturated flow problem based on Richards' equation. Our aim is to maximize the amount of absorbed liquid by redistributing the materials. To this end, a density based multi-material topology optimization (based on the well known SIMP model) is performed. A sensitivity analysis for the nonlinear transient problem is provided, which enables the application of first order optimization solvers. We perform two- and three-material optimization and discuss several variants of the problem setting. We present designs with up to 45% more absorbed liquid compared to the reference configuration. [ABSTRACT FROM AUTHOR]