Parallel Ant Colony Optimization Research Articles

Implementing a GPU-based parallel MAX–MIN Ant System

The MAX–MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore’s law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another key-component – the tabu list structure – which is used in the ACO’s solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

Developing parallel ant colonies filtered by deep learned constrains for predicting RNA secondary structure with pseudo-knots

RNA plays important roles in cells besides being a simple carrier of genetic information. RNA secondary structure prediction is an efficient way to explore its biochemical function. RNA secondary structure prediction with pseudo-knots is really difficult, which has been proven as an NP-hard problem. Many of the existing predictions seemed to be limited not only by the quality of the search algorithms but also by the quality of the objective functions used. In this paper, a novel prediction model called DpacoRNA is proposed to improve the accuracy of the RNA secondary structure prediction with pseudo-knots, which mainly consists of two features: a parallel search algorithm and learning structural constraints using a deep model. First, based on the base-paired and single-stranded probabilities as multiple objective functions, DpacoRNA applied a parallel ant colony optimization strategy to predict the RNA secondary structure. Second, a bi-directional LSTM recurrent neural network was used to learn the base-pairing constraints. Finally, the constraints learned from the deep model were applied to the output of parallel ant colonies to refine the final secondary structures. To examine the strength and the weakness of the proposed method, multiple RNA types, including RNase P RNA, 5s rRNA, hammerhead ribozyme, transfer RNA, and tmRNA, were used to carefully benchmark DpacoRNA with other state-of-the-art solutions. The final results showed that DpacoRNA was competitive to the other methods.

Development of a Convolution-Based Multi-Directional and Parallel Ant Colony Algorithm Considering a Network with Dynamic Topology Changes

While network path generation has been one of the representative Non-deterministic Polynomial-time (NP)-hard problems, changes of network topology invalidate the effectiveness of the existing metaheuristic algorithms. This research proposes a new and efficient path generation framework that considers dynamic topology changes in a complex network. In order to overcome this issue, Multi-directional and Parallel Ant Colony Optimization (MPACO) is proposed. Ant agents are divided into several groups and start at different positions in parallel. Then, Gaussian Process Regression (GPR)-based pheromone update method makes the algorithm more efficient. While the proposed MPACO algorithm is more efficient than the existing ACO algorithm, it is limited in a network with topological changes. In order to overcome the issue, the MPACO algorithm is modified to the Convolution MPACO (CMPACO) algorithm. The proposed algorithm uses the pheromone convolution method using a discrete Gaussian distribution. The proposed pheromone updating method enables the generation of a more efficient network path with comparatively less influence from topological network changes. In order to show the effectiveness of CMPACO, numerical networks considering static and dynamic conditions are tested and compared. The proposed CMPACO algorithm is considered a new and efficient parallel metaheuristic method to consider a complex network with topological changes.

Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem.

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time.

다방향 병렬 개미 군집화 알고리즘을 사용한 효과적인 경로 산출 프레임웍

복잡도가 높은 대규모 네트워크에서 경로를 산출하는 것은 네트웍의 복잡도가 증가함에 따라 지수적인 알고리즘 복잡도를 요구하는 대표적인 NP-Complete 문제로서, 노드 수가 많을 경우 대부분의 알고리즘이 성능이 저하되는 이슈를 보여준다. 이러한 이슈를 보완하기 위하여 본 연구에서의 기존의 개미 군집화 알고리즘을 보완하여, 새로운 개미 군집화 알고리즘인 다방향 병렬 개미 군집화 알고리즘을 제안한다. 일반적인 개미 군집화 알고리즘은 시작점에서 출구를 찾는 단일 출발 방식을 지향하는 하는 반면, 제시된 알고리즘은 다수의 지점에서 동시에 경로를 산출하며, 각 반복횟수 동안 출발지점을 효율적으로 업데이트 하는 기법을 제시한다. 이를 위해 세부적인 알고리즘 및 페로몬 업데이트, 탐색 노드의 업데이트 방법을 기술한다. 제시된 알고리즘의 유효성을 입증하기 위하여 미로형태의 수치제어 실험 및 이에 대한 분석을 기존 개미 군집화 알고리즘과 비교하여 분석하여 보여준다.

Autonomous vehicle routing problem solution based on artificial potential field with parallel ant colony optimization (ACO) algorithm

An autonomous vehicle routing problem solution (VRPS) algorithm with parallel ant colony optimization (ACO) considering pilot satisfaction is proposed in this paper to solve the problem in traditional autonomous vehicle routing, such as the service quality is affected by longer flying time of pilot due to too much emphasis on cost factors and ignoring of delivery time. First, the flying time of pilot is considered to improve traditional autonomous flight model so as to comprehensively consider autonomous cost and pilot satisfaction. Then, for NP problems in autonomous flight, a parallel ACO algorithm is used for global optimization and design Mapreduce improvement method of parallel ACO, to improve optimization performance of the algorithm and to obtain optimal global solution. At last, a contrast experiment is conducted and the results show that, the algorithm proposed herein can effectively reduce the flying time of pilot and help to improve the service quality.

Parallel Semi‐supervised enhanced fuzzy Co‐Clustering (PSEFC) and Rapid Association Rule Mining (RARM) based frequent route mining algorithm for travel sequence recommendation on big social media

SummaryIn this proposed method, with the aim of resolving the frequent route mining issue, by means of utilizing the Rapid Association Rule Mining (RARM) for frequent route mining, recurrently utilized routes as well as smaller distance routes are mined. As a result, support speedy decision of the route identify more willingly than the standard route optimization. Primarily, Multi‐Ontology based Points of Interest (MO‐POIs) model is presented that takes the POIs of user design in combination with the semantic info of the individual users. Furthermore, it as well takes another two steps that are along these lines: (1) routes ranking as per the similarity amid user package as well as routes packages are carried out by means of utilizing Parallel Semi‐supervised enhanced fuzzy Co‐Clustering (PSEFC) as well as (2) route optimizing by Parallel Ant Colony Optimization (PACO) technique in keeping with identical social users' records. The graph model is denoted as the amount of ants in the population of the identical user records. Assess the RARM‐PSEFC recommendation system on a set of Flickr images uploaded by users as well as travel POIs in numerous cities and show its efficiency.

Parallel ant colony optimization on multi-core SIMD CPUs

Ant colony optimization (ACO) is a population-based metaheuristic for solving hard combinatorial optimization problems. Many studies are dedicated to accelerating ACO by parallel hardware, especially by graphics processing units (GPUs). However, due to the irregular (random) pattern of ACO algorithms in data access and control flow, the performance of GPU-based approaches is constrained by hardware limitations. CPU-based SIMD computing for ACOs is rarely investigated in previous literatures, and how well multicore-SIMD CPU-based parallel ACOs could perform remains unknown. In this paper, we present and evaluate a model of vector parallel ACO for multi-core SIMD CPU architecture. In the proposed model, each ant is mapped with a CPU core and the tour construction of each ant is accelerated by vector instructions. Furthermore, based on the model, we propose a new fitness proportionate selection approach named Vector-based Roulette Wheel (VRW) in the tour construction stage. In this approach, the fitness values are grouped into SIMD lanes and the prefix sum is computed in vector-parallel mode. The proposed algorithm is tested on standard TSP instances ranging from 198 to 4461 cities and shows a speedup factor of 57.8x compared to the single-threaded CPU counterpart. More significantly, we compare our approach with high performance GPU-based ACOs, and the results demonstrate the strong potential of CPU-based parallel ACOs.

Dynamic strategy based parallel ant colony optimization on GPUs for TSPs

Dynamic strategy based parallel ant colony optimization on GPUs for TSPs

Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization

Ant colony optimisation (ACO) is a nature-inspired, population-based metaheuristic that has been used to solve a wide variety of computationally hard problems. In order to take full advantage of the inherently stochastic and distributed nature of the method, we describe a parallelization strategy that leverages these features on heterogeneous and large-scale, massively-parallel hardware systems. Our approach balances workload effectively, by dynamically assigning jobs to heterogeneous resources which then run ACO implementations using different search strategies. Our experimental results confirm that we can obtain significant improvements in terms of both solution quality and energy expenditure, thus opening up new possibilities for the development of metaheuristic-based solutions to real world problems on high-performance, energy-efficient contemporary heterogeneous computing platforms.

RMACO :a randomly matched parallel ant colony optimization

Ant Colony Optimization (ACO), inspired by the foraging behavior of real ants, is a widely applied bionic algorithm. Driven by the requirements of applications and the advances of computing technologies, ACO has been studied extensively, and the parallelism of ACO becomes an important research area. In this paper, we analyze the key factors that affect the performance of parallel ACO, based on which we propose a randomly matched parallel ant colony optimization (RMACO) using MPI. In RMACO, we design a new interconnection communication topology based on which the processors communicate with each other using a randomly matched method, and propose a non-fixed exchange cycle as well. All of these ensure the quality of the solution found by ACO and reduce the execution time. The experimental results show that RMACO has better efficiency compared with existing typical parallel ACO approaches.

Parallel ant colony optimization for the determination of a point heat source position in a 2-D domain

The Ant Colony Optimization (ACO) and its parallel implementations are applied to determine the unknown position of a point heat source in a two-dimensional steady-state heat conduction problem. The heuristic value, the determination of path and the objective function are all studied based on the inverse heat source problem. Some of the standard steps of the ACO are also modified according to the features of the heat conduction problem. In order to accelerate the speed of solving the inverse problem, two kinds of parallel ACO strategies, the fine-grained master-slave strategy and the coarse-grained strategy combined with the above modification are studied in this paper. The results show that the fine-grained strategy has a higher speedup than the coarse-grained strategy whiles the coarse-grained strategy has a higher accuracy rate. This means that the coarse-grained strategy is more proper during the parallel implementation for solving the inverse heat source problem and the parallel implementation of the ACO can improve the computational efficiency.

A Novel Approach using Parallel Ant Colony Optimization Algorithm for Detecting Routing Path based on Cluster Head in Wireless Sensor Network

More energy in wireless sensor network is spent in routing part as compare to other operation in sensor network. To route data in efficient manner detecting optimal path for routing is better solution. For time critical application, detection of optimal path should be faster enough to make the application efficient. This paper focus on detecting of optimal path by using parallel ant colony optimization algorithm. K-Mean clustering approach is used for grouping of sensor nodes because it is faster than other hierarchical clustering algorithm. Rotating the cluster head while implementing algorithm based on friss free space propagation model, increases the lifetime of the network. This proposed system is developed using Java. The results shows that proposed system detect the optimal path faster than existing system with better lifetime of the network.

GIẢI THUẬT KIẾN SONG SONG GIẢI BÀI TOÁN CÂY KHUNG NHỎ NHẤT CÓ BẬC BỊ CHẶN

GIẢI THUẬT KIẾN SONG SONG GIẢI BÀI TOÁN CÂY KHUNG NHỎ NHẤT CÓ BẬC BỊ CHẶN

Improved packing of protein side chains with parallel ant colonies

IntroductionThe accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains.MethodsWe present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library.ResultsWe focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of and 77.11% of angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains.ConclusionsThis parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms.

Parallel ant colony optimization for resource constrained job scheduling

In mining supply chains, large combinatorial optimization problems arise. These are NP-hard and typically require a large number of computing resources to solve them. In particular, the run-time overheads can become increasingly prohibitive with increasing problem sizes. Parallel methods provide a way to manage such run-time issues by utilising several processors in independent or shared memory architectures. However it is not obvious how to adapt serial optimisation algorithms to perform best in a parallel environment. Here, we consider a resource constrained scheduling problem which is motivated in mining supply chains and present two popular meta-heuristics, ant colony optimization (ACO) and simulated annealing and investigate how best to parallelize these methods on a shared memory architecture consisting of several cores. ACO’s solution construction framework is inherently parallel allowing a relatively straightforward parallel implementation. However, for best performance, ACO needs an element of local search. This significantly complicates the paralellization. Several alternative schemes for parallel ACO with elements of local search are considered and evaluated empirically. We find that ACO with local search is the most effective single-threaded algorithm. The best parallel implementation can obtain similar quality results to the serial method in significantly less elapsed time.

Comparative evaluation of platforms for parallel Ant Colony Optimization

The rapidly growing field of nature-inspired computing concerns the development and application of algorithms and methods based on biological or physical principles. This approach is particularly compelling for practitioners in high-performance computing, as natural algorithms are often inherently parallel in nature (for example, they may be based on a swarm-like model that uses a population of agents to optimize a function). Coupled with rising interest in nature-based algorithms is the growth in heterogenous computing; systems that use more than one kind of processor. We are therefore interested in the performance characteristics of nature-inspired algorithms on a number of different platforms. To this end, we present a new OpenCL-based implementation of the Ant Colony Optimization algorithm, and use it as the basis of extensive experimental tests. We benchmark the algorithm against existing implementations, on a wide variety of hardware platforms, and offer extensive analysis. This work provides rigorous foundations for future investigations of Ant Colony Optimization on high-performance platforms.

Using Improved Parallel Ant Colony Optimization Based on Graphic Processing Unit-Acceleration to Solve Motif Finding Problem

Using Improved Parallel Ant Colony Optimization Based on Graphic Processing Unit-Acceleration to Solve Motif Finding Problem

SoPC-Based Parallel ACO Algorithm and its Application to Optimal Motion Controller Design for Intelligent Omnidirectional Mobile Robots

This paper presents an embedded system-on-a-programmable-chip (SoPC)-based parallel ant colony optimization (ACO) algorithm and its application to optimal motion controller design for intelligent omnidirectional mobile robots. Both polar-space parallel ACO (PACO) parameter tuner and kinematic motion controller are integrated in one field programmable gate arrays chip to efficiently construct an intelligent omnidirectional mobile robot. The optimal parameters of the motion controllers are obtained by minimizing the performance index using the proposed SoPC-based PACO algorithm. These polar-space optimal parameters are then employed in the PACO-based embedded kinematic motion controller to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed PACO algorithm for embedded controllers of omnidirectional mobile robots. These results indicate that the proposed PACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm optimal controllers in polar coordinates.

Parallel ACO Algorithm on GPU for Fast Solution of QAPs

Recently, GPGPU (General Purpose computation on Graphics Processing Units) has become popular with great success, especially in scientific fields such as fluid dynamics, image processing, and visualization using particle methods. In this paper, we discuss how the GPGPU is used in implementations of parallel ant colony optimization (ACO) for fast solution of quadratic assignment problems (QAPs). As for the ACO, we use the cunning ant system (cAS) which is one of the most promising ACO algorithms.