1,126 research outputs found
Applying ACO To Large Scale TSP Instances
Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven
successful at solving Travelling Salesman Problems (TSP). However, ACO suffers
from two issues; the first is that the technique has significant memory
requirements for storing pheromone levels on edges between cities and second,
the iterative probabilistic nature of choosing which city to visit next at
every step is computationally expensive. This restricts ACO from solving larger
TSP instances. This paper will present a methodology for deploying ACO on
larger TSP instances by removing the high memory requirements, exploiting
parallel CPU hardware and introducing a significant efficiency saving measure.
The approach results in greater accuracy and speed. This enables the proposed
ACO approach to tackle TSP instances of up to 200K cities within reasonable
timescales using a single CPU. Speedups of as much as 1200 fold are achieved by
the technique
Improving the efficiency of photovoltaic cells embedded in floating buoys
Solar cells are used to power floating buoys, which is one of their applications. Floating buoys are devices that are placed on the sea and ocean surfaces to provide various information to the floats. Because these cells are subjected to varying environmental conditions, modeling and simulating photovoltaic cells enables us to install cells with higher efficiency and performance in them. The parameters of the single diode model are examined in this article so that the I-V, P-V diagrams, and characteristics of the cadmium telluride (CdTe) photovoltaic cell designed with three layers (CdTe, CdS, and SnOx) can be extracted using A solar cell capacitance simulator (SCAPS) software, and we obtain the parameters of the single diode model using the ant colony optimization (ACO) algorithm. In this paper, the objective function is root mean square error (RMSE), and the best value obtained after 30 runs is 5.2217×10-5 in 2.46 seconds per iteration, indicating a good agreement between the simulated model and the real model and outperforms many other algorithms that have been developed thus far. The above optimization with 200 iterations, a population of 30, and 84 points was completed on a server with 32 gigabytes of random-access memory (RAM) and 30 processing cores
Parallelization Strategies for Ant Colony Optimisation on GPUs
Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic
for the solution of a wide variety of problems. As a population-based
algorithm, its computation is intrinsically massively parallel, and it is
there- fore theoretically well-suited for implementation on Graphics Processing
Units (GPUs). The ACO algorithm comprises two main stages: Tour construction
and Pheromone update. The former has been previously implemented on the GPU,
using a task-based parallelism approach. However, up until now, the latter has
always been implemented on the CPU. In this paper, we discuss several
parallelisation strategies for both stages of the ACO algorithm on the GPU. We
propose an alternative data-based parallelism scheme for Tour construction,
which fits better on the GPU architecture. We also describe novel GPU
programming strategies for the Pheromone update stage. Our results show a total
speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone
update, and suggest that ACO is a potentially fruitful area for future research
in the GPU domain.Comment: Accepted by 14th International Workshop on Nature Inspired
Distributed Computing (NIDISC 2011), held in conjunction with the 25th
IEEE/ACM International Parallel and Distributed Processing Symposium (IPDPS
2011
A public transport bus assignment problem: parallel metaheuristics assessment
Combinatorial Optimization Problems occur in a wide variety of contexts and generally
are NP-hard problems. At a corporate level solving this problems is of great importance
since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario.
The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely,
we focus on the development of algorithms based on a sophisticated metaheuristic,
Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism.
For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information.
Our results show that our sequential ACO algorithms produced better solutions than
a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one
Differential evolution with an evolution path: a DEEP evolutionary algorithm
Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs
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