Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called e...
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Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimisation algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully be done by nine improved crossover operators. In addition, Cauchy mutation (CM) is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.
Conflicts between pedestrians and vehicles are one of the common safety issues at signalized intersections. Pedestrian Flashing GREEN (FG), a time interval for pedestrians on crosswalks to safely finish crossing befor...
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Conflicts between pedestrians and vehicles are one of the common safety issues at signalized intersections. Pedestrian Flashing GREEN (FG), a time interval for pedestrians on crosswalks to safely finish crossing before the next phase occurs, may fail to clear the crosswalk in the allotted time, due to significant pedestrian non-compliant behavior. In this manner, probability of pedestrian-vehicle exposures increases when non-compatible vehicle flows are released at the next immediate phase. This paper seeks to address this issue by presenting a traffic signal control strategy for urban traffic networks that aims to minimize vehicle traveling delay (increase efficiency) as well as pedestrian crossing risk (increase safety). First, a macroscopic model for pedestrian-vehicle mixed-flow networks is proposed. Considering the high-incidence rate of pedestrian violations during FG, an additional Dynamic All RED (DAR) phase is introduced at the end of each FG period, whose duration is adaptively adjusted according to the number of non-compliant pedestrians. With computational complexity being a concern for our model, an evolutionary algorithm with repairing mechanism (EARM), is proposed to solve our problem. Case studies are provided to illustrate the potential impact of the pedestrian movement to the vehicle traffic networks when pedestrian safety is considered in the system, as well as the efficacy of our traffic light control strategy for pedestrians and vehicles on risk reduction.
Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary datase...
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Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The fewmethods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a newevolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used k-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.
A mobile ad-hoc network (MANet) is a kind of wireless ad-hoc network, and is a self-configuring network of mobile routers (and associated hosts) connected by wireless links - The union of which form an arbitrary topol...
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Requirements discovery is the base of software engineering, and it has a great effect on the software development. The requirements are usually elicited through the communication with users in practice, but this metho...
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ISBN:
(纸本)1891706241
Requirements discovery is the base of software engineering, and it has a great effect on the software development. The requirements are usually elicited through the communication with users in practice, but this method is not suit for networked software. In this paper, we present a novel method for requirements discovery in networked software. It represents the initial requirement with tree based on RGPS (Role-Goal-Process-Service) and uses evolutionary algorithm to find other requirements similar to the given initial one. The experiment results show that the method proposed in this paper not only can find out the requirements similar to the initial one, but has better adaptability and higher efficiency than the traditional nested loop method used in the case of complex requirements and domain ontologies. It is promising.
One heuristic evolutionary algorithm recently proposed is the gravitational search algorithm (GSA), inspired by the gravitational forces between masses in nature. This algorithm has demonstrated superior performance a...
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One heuristic evolutionary algorithm recently proposed is the gravitational search algorithm (GSA), inspired by the gravitational forces between masses in nature. This algorithm has demonstrated superior performance among other well-known heuristic algorithms such as particle swarm optimisation and genetic algorithm. However, slow exploitation is a major weakness that might result in degraded performance when dealing with real engineering problems. Due to the cumulative effect of the fitness function on mass in GSA, masses get heavier and heavier over the course of iteration. This causes masses to remain in close proximity and neutralise the gravitational forces of each other in later iterations, preventing them from rapidly exploiting the optimum. In this study, the best mass is archived and utilised to accelerate the exploitation phase, ameliorating this weakness. The proposed method is tested on 25 unconstrained benchmark functions with six different scales provided by CEC 2005. In addition, two classical, constrained, engineering design problems, namely welded beam and tension spring, are also employed to investigate the efficiency of the proposed method in real constrained problems. The results of benchmark and classical engineering problems demonstrate the performance of the proposed method.
Software testing has its main goal as designing new test case sets in a manner in which it is able to depict its maximum faults. As soon as these test cases have been designed, Oracle software provides a method in whi...
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Software testing has its main goal as designing new test case sets in a manner in which it is able to depict its maximum faults. As soon as these test cases have been designed, Oracle software provides a method in which the software has to behave for a particular test case given. Prioritization of such test cases with the execution of their components specifying inputs, their operation and their outcome will determine as to whether the application and their properties are working in the right manner. The prioritization methods are as follows: initial ordering, random ordering and finally reverse ordering that were based on fault detection abilities. For developing software applications, a test suite that was less commonly known as the suite for checking the validity of software was employed. The test suite contained a detailed set of instructions and goals for each test case collection based on the system and its configuration used during testing. Automating the generation of a test case and test oracle was researched in an extensive manner. From among the automated test oracle, the artificial neural network (ANN) was used extensively but with a high cost of computation. This work proposed a weight optimized ANN using stochastic diffusion search to find the optimal weights with a unique fitness function such that computational time is reduced and misclassification rate reduced.
The El Nino Southern Oscillation (ENSO) is a major driver of global hydro-climatic variability, with well-known effects on floods, droughts, and coupled human-natural systems. Its impact on urban settlements depends o...
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The El Nino Southern Oscillation (ENSO) is a major driver of global hydro-climatic variability, with well-known effects on floods, droughts, and coupled human-natural systems. Its impact on urban settlements depends on both level of exposure and preparedness;two factors that are responsible for severe cuts on millions of people in developing countries, where urban water supply relies almost entirely on rainfall-dependent sources. To understand whether information on the ENSO state could help mitigate the effects of droughts, we use Metro Manila's water supply system as exemplifying case study, for which we design "traditional" and adaptive management policies. The former are based on information typically available to operators, such as reservoir storage;the latter complement such information with the Oceanic Nino Index (ONI)-an indicator used for monitoring El Nino and La Nina state. Results obtained by comparing the policy performance on a large set of stochastic streamflow and ONI replicates show that ENSO-informed policies are more robust, meaning that they attain a minimum performance level across a broader set of replicates. We show that the primary cause of this behavior is the information on the ENSO state. To further quantify the value of the ONI, we then compare the performance of a representative ENSO-informed policy and the system's current operating rules on the period 1968-2014. The comparison shows that the severe water supply restrictions caused by the existing management system could have been partially avoided through a sequence of smaller restrictions implemented at the onset of the main El Nino events.
A novel genetic algorithm is described in this paper for the problem of constrained optimization. The algorithm incorporates modified genetic operators that preserve the feasibility of the trial solutions encoded in t...
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A novel genetic algorithm is described in this paper for the problem of constrained optimization. The algorithm incorporates modified genetic operators that preserve the feasibility of the trial solutions encoded in the chromosomes, the stochastic application of a local search procedure and a stopping rule which is based on asymptotic considerations. The algorithm is tested on a series of well-known test problems and a comparison is made against the algorithms C-SOMGA and DONLP2. (C) 2008 Elsevier Inc. All rights reserved.
This work addresses the Minimum Subgraph Diameter Problem (MSDP), an NP-hard problem with applications to network design. Given an undirected graph with lengths and costs on the edges, the MSDP's goal is to find a...
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This work addresses the Minimum Subgraph Diameter Problem (MSDP), an NP-hard problem with applications to network design. Given an undirected graph with lengths and costs on the edges, the MSDP's goal is to find a spanning subgraph with total cost limited by a given budget, such that the subgraph's diameter is minimum. We propose a metaheuristic approach using a Biased Random-Key Genetic Algorithm for the MSDP, with three different decoders, diameter computation caching and warm-start solutions. These are the first heuristic approaches for the MSDP, and we conduct a computational study on a previously established benchmark. The results show our proposed methods contribute significantly to known optimality gaps and are better than the state-of-the-art methods for large instances in solution quality.
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