An introduction to articles published within the issue is presented, including one by Phil Evers et on systems modeling, another by Stanley Griffis et al on ant colony optimization and metaheuristics, and one by Dale ...
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An introduction to articles published within the issue is presented, including one by Phil Evers et on systems modeling, another by Stanley Griffis et al on ant colony optimization and metaheuristics, and one by Dale Rogers et al on reverse logistics.
The design of routing protocols for mobile ad hoc networks (MANETs) is a complex task given the dynamic nature of such networks. Particular types of routing protocols are known as bioinspired. Related to theses, the a...
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The design of routing protocols for mobile ad hoc networks (MANETs) is a complex task given the dynamic nature of such networks. Particular types of routing protocols are known as bioinspired. Related to theses, the algorithms based on ant Colony Optimization (ACO), are particularly relevant. This work presents a new variant of antOR, a multihop adaptive routing protocol based on antHocNet which already has two versions: disjoint link routes (antOR-DLR) and disjoint node (antOR-DNR). The new protocol, called antOR-RDLR, differs from antOR-DLR in the pheromones updating process and the route discovery mechanism. The simulation results indicate that antOR-RDLR improves their predecessors in all analyzed metrics.
Due to intrinsic properties of aqueous environments, routing protocols for underwater wireless sensor network (UWSN) have to cope with many challenges such as long propagation delay, bad robustness, and high energy co...
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Due to intrinsic properties of aqueous environments, routing protocols for underwater wireless sensor network (UWSN) have to cope with many challenges such as long propagation delay, bad robustness, and high energy consumption. Basic ant colony optimization algorithm (ACOA) is an intelligent heuristic algorithm which has good robustness, distributed computing and combines with other algorithms easily. But its disadvantage is that it may converge at local solution, not global solution. Artificial fish swarm algorithm (AFSA) is one kind of intelligent algorithm that can converge at global solution set quickly but has lower precision in finding global solution. Therefore we can make use of AFSA and ACOA based on idea of complementary advantages. So ACOA-AFSA fusion routing algorithm is proposed which possesses advantages of AFSA and ACOA. As fusion algorithm has aforementioned virtues, it can reduce existing routing protocols' transmission delay, energy consumption and improve routing protocols' robustness theoretically. Finally we verify the feasibility and effectiveness of fusion algorithm through a series of simulations.
The design of routing protocols for mobile ad hoc networks (MANETs) is a complex task given the dynamic nature of such networks. Particular types of routing protocols are known as bioinspired. This work presents a par...
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The design of routing protocols for mobile ad hoc networks (MANETs) is a complex task given the dynamic nature of such networks. Particular types of routing protocols are known as bioinspired. This work presents a parallelization of antOR-DNR, a bioinspired routing protocol for mobile ad hoc networks based on the ant Colony Optimization (ACO) algorithm. This new protocol, called PantOR-MI, uses, as well as PantOR, the thread programming based on shared memory. This new parallelization is applied in route discovery phases, route local repair process, and link failure notification. The simulation results indicate that PantOR and PantOR-MI improve performances of antOR, whilst it is also noticed that PantOR-MI is the most suitable for highly dynamic environments.
This paper introduces two improved forms of the ant colony optimization (ACO) algorithm applied to a proportional integral derivative (PID) controller and Smith predictor design. Derivative free optimization methods, ...
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This paper introduces two improved forms of the ant colony optimization (ACO) algorithm applied to a proportional integral derivative (PID) controller and Smith predictor design. Derivative free optimization methods, namely simplex derivative based pattern search (SDPS) and implicit filtering (IMF), are used to intensify the search mechanism in the ACO algorithm with improved convergence over the original ACO. The effectiveness of the controller schemes using the proposed algorithms, namely SDPS-ACO, and IMF-ACO, is demonstrated using unit step set point response for a class of dead-time systems, and the results are compared with some existing methods of controller tuning.
The designing of polypeptides with novel conduction properties is important because of the role of proteins in molecular electronics. ant algorithm which is based on the cooperative interaction between artificial ants...
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The designing of polypeptides with novel conduction properties is important because of the role of proteins in molecular electronics. ant algorithm which is based on the cooperative interaction between artificial ants has proved to be an effective tool for such designing. It has been used in the present work in combination with simple negative factor counting and inverse iteration methods to study the effect of basis set, electron correlation and hydration on the electronic properties and hence the optimum compositions of polyglycine, polyalanine and polyserine in the most conducting protein chain. The results show clearly that a better basis set and the consideration of correlation decreases the band gap by about 1-3 eV. The optimum solution however remains unaffected. Further, using minimal basis set in the presence of water decreases the band gap by about 2.64 eV and changes the major component of the conducting protein from polyserine to polyalanine. (c) 2010 Elsevier B.V. All rights reserved.
This paper presents evolutionary approaches for designing rotational inverted pendulum (RIP) controller including genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) methods. ...
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This paper presents evolutionary approaches for designing rotational inverted pendulum (RIP) controller including genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) methods. The goal is to balance the pendulum in the inverted position. Simulation and experimental results demonstrate the robustness and effectiveness of the proposed controllers with regard to parameter variations, noise effects, and load disturbances. The proposed methods can be considered as promising ways for control of various similar nonlinear systems.
A general model is presented to unify the explanation of different meta-heuristic algorithms. This model is based on the concept of fields of forces from physics and covers many meta-heuristic algorithms consisting of...
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A general model is presented to unify the explanation of different meta-heuristic algorithms. This model is based on the concept of fields of forces from physics and covers many meta-heuristic algorithms consisting of Genetic algorithms, ant Colony Optimization, Particle Swarm Optimization, Big Bang-Big Crunch algorithm and Harmony Search. The properties of these algorithms can be explained using the presented general model that is called the fields of forces (FOF) model. This extension provides efficient means to improve, expand, modify and hybridize the meta-heuristic algorithms. An improved and hybridized algorithm is then developed using the FOF model.
This paper considersthe optimisation of the movement of a fixed crane operating in a single aisle of a distribution centre. The crane must move pallets in inventory between docking bays, storage locations, and picking...
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This paper considersthe optimisation of the movement of a fixed crane operating in a single aisle of a distribution centre. The crane must move pallets in inventory between docking bays, storage locations, and picking lines. Both a static and a dynamic approach to the problem are presented. The optimisation is performed by means of tabu search, ant colony metaheuristics, and hybrids of these two methods. All these solution approaches were tested on real life data obtained from an operational distribution centre. Results indicate that the hybrid methods outperform the other approaches.
In social insects, the superposition of simple individual behavioral rules leads to the emergence of complex collective patterns and helps solve difficult problems inherent to surviving in hostile habitats. Modelling ...
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In social insects, the superposition of simple individual behavioral rules leads to the emergence of complex collective patterns and helps solve difficult problems inherent to surviving in hostile habitats. Modelling ant colony foraging reveals strategies arising from the insects' self-organization and helps develop of new computational strategies in order to solve complex problems. This paper presents advances in modelling ants' behavior when foraging in a confined and dynamic environment, based on experiments with the Argentine ant Linepithema humile in a relatively complex artificial network. We propose a model which overcomes the problem of stagnation observed in earlier models by taking into account additional biological aspects, by using non-linear functions for the deposit, perception and evaporation of pheromone, and by introducing new mechanisms to represent randomness and the exploratory behavior of the ants. (C) 2011 Elsevier Ireland Ltd. All rights reserved.
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