In this paper, the evolutionary algorithm is applied to obtain the adaptive wavelet feature parameters to represent High Resolution Range Profile (HRRP) andtrain the adaptive wavelet neural network to classify HRRP. T...
详细信息
Lennard-Jones clusters are the best-known benchmark for global cluster structure optimization. For a few cluster sizes, the landscape is deceptive, featuring several funnels, with the global minimum not being in the w...
详细信息
Lennard-Jones clusters are the best-known benchmark for global cluster structure optimization. For a few cluster sizes, the landscape is deceptive, featuring several funnels, with the global minimum not being in the widest one. More than a decade ago, several non-deterministic global search algorithms were presented that could solve these cases, mostly using additional tools to ensure structural diversity. Recently, however, many publications have advertised new search algorithms, claiming efficiency but being unable to solve these harder benchmark cases. Here, we demonstrate that evolutionary algorithms can solve these hard cases efficiently, if enhanced with one of several very different diversity measures (niching) which were set up in an ad-hoc way, without extensive deliberation, testing or tuning. Hence, these hard benchmark cases should definitely be considered solvable. Additionally, these niching concepts offer insights into the different Lennard-Jones structural types, and into the way niching works in evolutionary algorithms. (C) 2016 Elsevier B.V. All rights reserved.
To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. ...
详细信息
To reveal heterogeneous behaviors of opinion evolution in different scenarios, we propose an opinion model with topic interactions. Individual opinions and topic features are represented by a multidimensional vector. We measure an agent's action towards a specific topic by the product of opinion and topic feature. When pairs of agents interact for a topic, their actions are introduced to opinion updates with bounded confidence. Simulation results show that a transition from a disordered state to a consensus state occurs at a critical point of the tolerance threshold, which depends on the opinion dimension. The critical point increases as the dimension of opinions increases. Multiple topics promote opinion interactions and lead to the formation of macroscopic opinion clusters. In addition, more topics accelerate the evolutionary process and weaken the effect of network topology. We use two sets of large-scale real data to evaluate the model, and the results prove its effectiveness in characterizing a real evolutionary process. Our model achieves high performance in individual action prediction and even outperforms state-of-the-art methods. Meanwhile, our model has much smaller computational complexity. This paper provides a demonstration for possible practical applications of theoretical opinion dynamics. Published by AIP Publishing.
Without doubt, one of the powerful and effective optimiser in the area of evolutionary algorithms and improved particle swarm optimisation (PSO) is the self-organising hierarchical PSO with time-varying acceleration c...
详细信息
Without doubt, one of the powerful and effective optimiser in the area of evolutionary algorithms and improved particle swarm optimisation (PSO) is the self-organising hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC) which has been implemented successfully in the many problems (cited by 2430 until now). Real-world problems are multi-variable problems with real-world different complexities. The classical HPSO-TVAC optimisation technique often converges to local optima solution for some of the real-world problems. Therefore, finding efficient modern versions of the PSO algorithm (here HPSO-TVAC) to solve the real-world problems are absorbing a growing attention in recent years. A novel HPSO-TVAC algorithm for real-world optimisation is proposed. The simulation results show that proposed HPSO-TVAC new version, NHPSO-JTVAC, is powerful and very competitive for real-world optimisation.
In this paper, a novel swarm intelligence-based ensemble metaheuristic optimization algorithm, called Structured Clanning-based Ensemble Optimization, is proposed for solving complex numerical optimization problems. T...
详细信息
In this paper, a novel swarm intelligence-based ensemble metaheuristic optimization algorithm, called Structured Clanning-based Ensemble Optimization, is proposed for solving complex numerical optimization problems. The proposed algorithm is inspired by the complex and diversified behaviour present within the fission-fusion-based social structure of the elephant society. The population of elephants can consist of various groups with relationship between individuals ranging from mother-child bond, bond groups, independent males, and strangers. The algorithm tries to model this individualistic behaviour to formulate an ensemble-based optimization algorithm. To test the efficiency and utility of the proposed algorithm, various benchmark functions of different geometric properties are used. The algorithm performance on these test benchmarks is compared to various state-of-the-art optimization algorithms. Experiments clearly showcase the success of the proposed algorithm in optimizing the benchmark functions to better values.
One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the ...
详细信息
One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like GA, PSO have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an accelerating particle swarm optimization algorithm (APSO) is applied to generate test data for data-flow dependencies of a program guided by a new fitness function. APSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology ERP system were taken for the experimental analysis. The experimental results show that the proposed APSO based approach performed significantly better than random search, genetic algorithm and PSO in enhancing the convergence speed.
This paper addresses the problem of resource distribution control in logistic systems influenced by uncertain demand. The considered class of logistic topologies comprises two types of actorscontrolled nodes and exter...
详细信息
This paper addresses the problem of resource distribution control in logistic systems influenced by uncertain demand. The considered class of logistic topologies comprises two types of actorscontrolled nodes and external sourcesinterconnected without any structural restrictions. In this paper, the application of continuous-domain genetic algorithms (GAs) is proposed in order to support the optimization process of resource reflow in the network channels. GAs allow one to perform simulation-based optimization and provide desirable operating conditions in the face of a priori unknown, time-varying demand. The effectiveness of inventory management process governed under an order-up-to policy involves two different objectivesholding costs and service level. Using the network analytical model with the inventory management policy implemented in a centralized way, GAs search a space of candidate solutions to find optimal policy parameters for a given topology. Numerical experiments confirm the analytical assumptions.
In this paper, a Takagi-Sugeno-Kang (TSK) fuzzy inference system using fuzzy c-means clustering and differential evolution optimization is proposed and validated when applied to a twin rotor system (TRS). The TRS is p...
详细信息
A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biologic...
详细信息
A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known "S curve", with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.
Facial composite is a tool used in criminal investigation to identify an unknown person, usually a suspect of a crime. Traditionally, it is created by a forensic artist on the basis of a detailed description provided ...
详细信息
暂无评论