The self-organizing worm algorithm

A new multi-modal optimization algorithm called the self-organizing worm algorithm （SOWA） is presented for optimization of multi-modal functions. The main idea of this algorithm can be described as follows： disperse some worms equably in the domain; the worms exchange the information each other and creep toward the nearest high point; at last they will stop on the nearest high point. All peaks of multi-modal function can be found rapidly through studying and chasing among the worms. In contrast with the classical multi-modal optimization algorithms, SOWA is provided with a simple calculation, strong convergence, high precision, and does not need any prior knowledge. Several simulation experiments for SOWA are performed, and the complexity of SOWA is analyzed amply. The results show that SOWA is very effective in optimization of multi-modal functions.

A computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory

Although emission spectral tomography （EST） combines emission spectral measurement with optical computed tomography （OCT）, it is difficult to gain transient emission data from a large number of views, therefore, high precision OCT algorithms with few views ought to be studied for EST application. To improve the reconstruction precision in the case of few views, a new computed tomography reconstruction algorithm based on multipurpose optimal criterion and simulated annealing theory （multi-criterion simulated annealing reconstruction technique, MCSART） is proposed. This algorithm can suffice criterion of least squares, criterion of most uniformity, and criterion of most smoothness synchronously. We can get global optimal solution by MCSART algorithm with simulated annealing theory. The simulating experiment result shows that this algorithm is superior to the traditional algorithms under various noises.

A Game Theoretic Approach for a Minimal Secure Dominating Set

The secure dominating set(SDS),a variant of the dominating set,is an important combinatorial structure used in wireless networks.In this paper,we apply algorithmic game theory to study the minimum secure dominating set(Min SDS) problem in a multi-agent system.We design a game framework for SDS and show that every Nash equilibrium(NE) is a minimal SDS,which is also a Pareto-optimal solution.We prove that the proposed game is an exact potential game,and thus NE exists,and design a polynomial-time distributed local algorithm which converges to an NE in O(n) rounds of interactions.Extensive experiments are done to test the performance of our algorithm,and some interesting phenomena are witnessed.

CREATION OF OPTIMAL MOVEMENT STRATEGY OF PLURAL MOVING OB-JECTS BY GA

The topographic information of a closed world is expressed as a graph. The plural mov- ingobjects which go and back in it according to a single moving strategy are supposed.The moving strategy is expressed by numerical values as a decision table. Coding is performed with this table as chromosomes, and this is optimized by using genetic algorithm. These environments were realized on a computer, and the simulation was carried out. As the result, the learning of the method to act so that moving objects do not obstruct mutually was recognized, and it was confirmed that these methods are effective for optimizing moving strategy.

Multi-objective optimization of crimping of large-diameter welding pipe

Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.

Novel Lossless Compression Method Based on the Fourier Transform to Approximate the Kolmogorov Complexity of Elementary Cellular Automata

We propose a novel, lossless compression algorithm, based on the 2D Discrete Fast Fourier Transform, to approximate the Algorithmic (Kolmogorov) Complexity of Elementary Cellular Automata. Fast Fourier transforms are widely used in image compression but their lossy nature exclude them as viable candidates for Kolmogorov Complexity approximations. For the first time, we present a way to adapt fourier transforms for lossless image compression. The proposed method has a very strong Pearsons correlation to existing complexity metrics and we further establish its consistency as a complexity metric by confirming its measurements never exceed the complexity of nothingness and randomness (representing the lower and upper limits of complexity). Surprisingly, many of the other methods tested fail this simple sanity check. A final symmetry-based test also demonstrates our method’s superiority over existing lossless compression metrics. All complexity metrics tested, as well as the code used to generate and augment the original dataset, can be found in our github repository: ECA complexity metrics1.

Algorithm fusion to improve detection of lung cancer on chest radiographs

Purpose-The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer.Various image processing algorithms are presented for different types of lesions,and a scheme is proposed for the combination of results.Design/methodology/approach-A computer aided detection(CAD)scheme was developed for detection of lung cancer.It enables different lesion enhancer algorithms,sensitive to specific lesion subtypes,to be used simultaneously.Three image processing algorithms are presented for the detection of small nodules,large ones,and infiltrated areas.The outputs are merged,the false detection rate is reduced with four separated support vector machine(SVM)classifiers.The classifier input comes from a feature selection algorithm selecting from various textural and geometric features.A total of 761 images were used for testing,including the database of the Japanese Society of Radiological Technology(JSRT).Findings-The fusion of algorithms reduced false positives on average by 0.6 per image,while the sensitivity remained 80 per cent.On the JSRT database the system managed to find 60.2 per cent of lesions at an average of 2.0 false positives per image.The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured.The system was compared to other published methods.Originality/value-The study described in the paper proves the usefulness of lesion enhancement decomposition,while proposing a scheme for the fusion of algorithms.Furthermore,a new algorithm is introduced for the detection of infiltrated areas,possible signs of lung cancer,neglected by previous solutions.

H-infinity control for air-breathing hypersonic vehicle based on online simultaneous policy update algorithm

Purpose–The air-breathing hypersonic vehicle(AHV)includes intricate inherent coupling between the propulsion system and the airframe dynamics,which results in an intractable nonlinear system for the controller design.The purpose of this paper is to propose an H1 control method for AHV based on the online simultaneous policy update algorithm(SPUA).Design/methodology/approach–Initially,the H1 state feedback control problem of the AHV is converted to the problem of solving the Hamilton-Jacobi-Isaacs(HJI)equation,which is notoriously difficult to solve both numerically and analytically.To overcome this difficulty,the online SPUA is introduced to solve the HJI equation without requiring the accurate knowledge of the internal system dynamics.Subsequently,the online SPUA is implemented on the basis of an actor-critic structure,in which neural network(NN)is employed for approximating the cost function and a least-square method is used to calculate the NN weight parameters.Findings–Simulation study on the AHV demonstrates the effectiveness of the proposed H1 control method.Originality/value–The paper presents an interesting method for the H1 state feedback control design problem of the AHV based on online SPUA.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

Social Choice Meets Graph Drawing: How to Get Subexponential Time Algorithms for Ranking and Drawing Problems

We analyze a common feature of p-Kemeny AGGregation（p-KAGG） and p-One-Sided Crossing Minimization（p-OSCM） to provide new insights and findings of interest to both the graph drawing community and the social choice community. We obtain parameterized subexponential-time algorithms for p-KAGG—a problem in social choice theory—and for p-OSCM—a problem in graph drawing. These algorithms run in time O＊（2O（√k log k））,where k is the parameter, and significantly improve the previous best algorithms with running times O.1.403k/and O.1.4656k/, respectively. We also study natural ＂above-guarantee＂ versions of these problems and show them to be fixed parameter tractable. In fact, we show that the above-guarantee versions of these problems are equivalent to a weighted variant of p-directed feedback arc set. Our results for the above-guarantee version of p-KAGG reveal an interesting contrast. We show that when the number of ＂votes＂ in the input to p-KAGG is odd the above guarantee version can still be solved in time O＊（2O（√k log k））, while if it is even then the problem cannot have a subexponential time algorithm unless the exponential time hypothesis fails（equivalently, unless FPT D M[1]）.

Receding horizon control for cooperative search of multi-UAVs based on differential evolution

Purpose–The purpose of this paper is to present a hybrid method of intelligent optimization algorithm and Receding Horizon Control.The method is applied to solve the problem of cooperative search of multi-unmanned aerial vehicles(multi-UAVs).Design/methodology/approach–The intelligent optimization of Differential Evolution(DE)makes the complex problem of multi-UAVs cooperative search a regular function optimization problem.To meet the real-time requirement,the idea of Receding Horizon Control is applied.An Extended Search Map based on hormone information is used to describe the uncertain environment information.Findings–Simulation results indicate effectiveness of the hybrid method in solving the problem of cooperative search for multi-UAVs.Originality/value–The paper presents an interesting hybrid method of DE and Receding Horizon Control for the problem of cooperative multi-UAVs.

Equidistance target-following controller for underactuated autonomous underwater vehicles

Purpose–Autonomous Underwater Vehicles(AUVs)play a crucial role in marine biology research and oceanic natural resources exploration.Since most AUVs are underactuated they require sophisticated trajectory planning and tracking algorithms.The purpose of this paper is to develop a new method that allows an underactuated AUV to track a moving object while constraining the approach to a direction tangent to the path of the target.Furthermore,the distance at which the AUV follows the target is constrained,reducing the probability of detection and unwanted behavior change of the target.Design/methodology/approach–First,a kinematic controller that generates a trajectory tangent to the path of the moving target is designed such that the AUV maintains a prescribed distance and approaches the target from behind.Using a Lyapunov based method the stability of the kinematic controller is proven.Second,a dynamic sliding mode controller is employed to drive the vehicle on the trajectory computed in the first step.Findings–The kinematic and dynamic controllers are shown to be stable and robust against parameter uncertainty in the dynamic model of the vehicle.Results of numerical simulations for equidistant tracking of a target on both smooth and discontinuous derivatives trajectories for a variety of relative initial positions and orientations are shown.Originality/value–The contribution of this research is development of a new method for path planning and tracking of moving targets for underactuated AUVs in the horizontal plane.The method allows control of both the direction of approach and the distance from a moving object.

A version of Geiringer-like theorem for decision making in the environments with randomness and incomplete information

Purpose-The purpose of this paper is to establish a version of a theorem that originated from population genetics and has been later adopted in evolutionary computation theory that will lead to novel Monte-Carlo sampling algorithms that provably increase the AI potential.Design/methodology/approach-In the current paper the authors set up a mathematical framework,state and prove a version of a Geiringer-like theorem that is very well-suited for the development of Mote-Carlo sampling algorithms to cope with randomness and incomplete information to make decisions.Findings-This work establishes an important theoretical link between classical population genetics,evolutionary computation theory and model free reinforcement learning methodology.Not only may the theory explain the success of the currently existing Monte-Carlo tree sampling methodology,but it also leads to the development of novel Monte-Carlo sampling techniques guided by rigorous mathematical foundation.Practical implications-The theoretical foundations established in the current work provide guidance for the design of powerful Monte-Carlo sampling algorithms in model free reinforcement learning,to tackle numerous problems in computational intelligence.Originality/value-Establishing a Geiringer-like theorem with non-homologous recombination was a long-standing open problem in evolutionary computation theory.Apart from overcoming this challenge,in a mathematically elegant fashion and establishing a rather general and powerful version of the theorem,this work leads directly to the development of novel provably powerful algorithms for decision making in the environment involving randomness,hidden or incomplete information.

A New Approach to Graph Recognition and Applications to Distance-Hereditary Graphs

Algorithms used in data mining and bioinformatics have to deal with huge amount of data efficiently. In many applications, the data are supposed to have explicit or implicit structures. To develop efficient algorithms for such data, we have to propose possible structure models and test if the models are feasible. Hence, it is important to make a compact model for structured data, and enumerate all instances efficiently. There are few graph classes besides trees that can be used for a model. In this paper, we investigate distance-hereditary graphs. This class of graphs consists of isometric graphs and hence contains trees and cographs. First, a canonical and compact tree representation of the class is proposed. The tree representation can be constructed in linear time by using prefix trees. Usually, prefix trees are used to maintain a set of strings. In our algorithm, the prefix trees are used to maintain the neighborhood of vertices, which is a new approach unlike the lexicographically breadth-first search used in other studies. Based on the canonical tree representation, efficient algorithms for the distance-hereditary graphs are proposed, including linear time algorithms for graph recognition and graph isomorphism and an efficient enumeration algorithm. An efficient coding for the tree representation is also presented; it requires [3.59n] bits for a distance-hereditary graph of n vertices and 3n bits for a cograph. The results of coding improve previously known upper bounds （both are 2^O（nlogn）） of the number of distance-hereditary graphs and cographs to 2^[3.59n] and 2^3n, respectively.