Hybrid Ant Colony Optimization Algorithm Research Articles

Joint Resource Allocation and Relay Selection in LTE-Advanced Network Using Hybrid Co-Operative Relaying and Network Coding

The problem of joint resource allocation and relay selection is studied for bidirectional LTE-advanced relay networks. The bidirectional communication between user equipment (UE) and eNodeB (eNB) is performed via direct transmission, co-operative relaying (CoR), or a combination of network coding (NC) and CoR (NC/CoR). In this paper, an enhanced three-time-slot per cycle time-division duplexing (TDD) scheme is proposed for LTE-Advanced frame architecture to accommodate a hybrid transmission scheme. More specifically, we formulate the problem of joint resource assignment, relay selection, and bidirectional transmission scheme selection as a combinatorial optimization problem with the objective to maximize the total product of backlog and rate (back-pressure principle). Two approaches are considered to solve our combinatorial optimization problem. First, a graph-based framework is proposed in which the problem is transformed into a maximum weighted Clique problem (MWCP). In addition, our problem is also transformed into a three-dimensional assignment problem (3DAP) which is solved using a hybrid ant colony optimization (ACO) algorithm. Using simulations, it is concluded that the hybrid transmission scheme outperforms all conventional nonhybrid schemes. Moreover, the simulation results confirm that while the two proposed solutions provide similar results, the ACO algorithm is faster due to its lower complexity.

English

English

Solving a Closed-Loop Location-Inventory-Routing Problem with Mixed Quality Defects Returns in E-Commerce by Hybrid Ant Colony Optimization Algorithm

This paper presents a closed-loop location-inventory-routing problem model considering both quality defect returns and nondefect returns in e-commerce supply chain system. The objective is to minimize the total cost produced in both forward and reverse logistics networks. We propose a combined optimization algorithm named hybrid ant colony optimization algorithm (HACO) to address this model that is an NP-hard problem. Our experimental results show that the proposed HACO is considerably efficient and effective in solving this model.

A novel hybrid ant colony optimization and particle swarm optimization algorithm for inverse problems of coupled radiative and conductive heat transfer

In this study, a continuous ant colony optimization algorithm on the basis of probability density function was applied to the inverse problems of one-dimensional coupled radiative and conductive heat transfer. To overcome the slow convergence of the ant colony optimization algorithm for continuous domain problems, a novel hybrid ant colony optimization and particle swarm optimization algorithm was proposed. To illustrate the performances of these algorithms, the thermal conductivity, absorption coefficient and scattering coefficient of the one-dimensional homogeneous semi-transparent medium were retrieved for several test cases. The temperature and radiative heat flux simulated by the finite volume method were served as inputs for the inverse analysis. Through function estimation and parameter estimation, the HAPO algorithm was proved to be effective and robust.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

Hybrid Particle Swarm Optimization and Ant Colony Optimization Technique for the Optimal Design of Shell and Tube Heat Exchangers

Abstract Owing to the wide utilization of shell and tube heat exchangers (STHEs) in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until satisfying a given heat duty and set of geometric and operational constraints. Although well proven, this kind of approach is time-consuming and may not lead to cost-effective design. The present study explores the use of non-traditional optimization technique called hybrid particle swarm optimization (PSO) and ant colony optimization (ACO), for design optimization of STHEs from economic point of view. The PSO applies for global optimization and ant colony approach is employed to update positions of particles to attain rapidly the feasible solution space. ACO works as a local search, wherein ants apply pheromone-guided mechanism to update the positions found by the particles in the earlier stage. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut, etc. and minimization of total annual cost is considered as design target. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm. The examples analyzed show that the hybrid PSO and ACO algorithm provides a valuable tool for optimal design of heat exchanger. The hybrid PSO and ACO approach is able to reduce the total cost of heat exchanger as compare to cost obtained by previously reported genetic algorithm (GA) approach. The result comparisons with particle swarm optimizer and other optimization algorithms (GA) demonstrate the effectiveness of the presented method.

An improved hybrid ant-local search algorithm for the partition graph coloring problem

In this paper we propose a hybrid Ant Colony Optimization (ACO) algorithm for the Partition Graph Coloring Problem (PGCP). Given an undirected graph G=(V,E), whose nodes are partitioned into a given number of the sets, the goal of the PGCP is to find a subset V∗⊂V that contains exactly one node from each cluster and a coloring for V∗ so that in the graph induced by V∗, two adjacent nodes have different colors and the total number of used colors is minimal. Our hybrid algorithm is obtained by executing a local search procedure after every ACO iteration. The performance of our algorithm is evaluated on a set of instances commonly used as benchmark and the computational results show that compared to state-of-the-art algorithms, our improved hybrid ACO algorithm achieves solid results in very short run-times.

A hybrid ant colony optimisation algorithm for job shop problems and its convergence analysis

This paper presents a hybrid ant colony optimisation (HACO) algorithm for solving job shop problems. The criterion considered is the maximum completion time, the so-called makespan. The HACO algorithm improves the performance of intelligence optimisation algorithm, which adopts ant colony optimisation (ACO) algorithm to search in the global solution space, and tabu search (TS) algorithm is utilised as the local algorithm in each generation. The global asymptotic convergence of the hybrid algorithm is proved by Markov chain theory in the paper. By testing 13 hard benchmarks instance, the results demonstrate that the HACO algorithm is effective.

An Effective Hybrid Ant Colony Optimization for Permutation Flow-Shop Scheduling

This paper proposes a hybrid ant colony optimization algorithm to solve the permutation flow-shop scheduling (PFS). The hybridization of ant colony optimization (ACO) with path relinking (PR), which combines the advantages of two individual algorithms, is the key innovative aspect of the approach. Path relinking (PR) can be interpreted as an evo- lutionary method where the high quality solutions are generated by introducing features of the guiding solution gradually into the initial solution. Moreover, the effective hybrid algorithm is a method to integrate intensification and diversifica- tion in the search, and it adopts the criterion function restricting the frequencies of using the PR procedure to improve the convergence speed. Finally, the proposed algorithm is applied to PFS benchmark problems. The experimental results have shown that the hybrid method yields better results to solve the permutation flow-shop scheduling than well-known exist- ing methods in terms of solution quality.

Smart data packet ad hoc routing protocol

This paper introduces a smart data packet routing protocol (SMART) based on swarm technology for mobile ad hoc networks. The main challenge facing a routing protocol is to cope with the dynamic environment of mobile ad hoc networks. The problem of finding best route between communication end points in such networks is an NP problem. Swarm algorithm is one of the methods used solve such a problem. However, copping with the dynamic environment will demand the use of a lot of training iterations. We present a new infrastructure where data packets are smart enough to guide themselves through best available route in the network. This approach uses distributed swarm learning approach which will minimize convergence time by using smart data packets. This will decrease the number of control packets in the network as well as it provides continues learning which in turn provides better reaction to changes in the network environment. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive packets in order to maintain and find better routes. This protocol is a hybrid Ant Colony Optimization (ACO) and river formation dynamics (RFD) swarm algorithms protocol. ACO is used to set up multi-path routes to destination at the initialization, while RFD mainly used as a base algorithm for the routing protocol. RFD offers many advantages toward implementing this approach. The main two reasons of using RFD are the small amount of information that required to be added to the packets (12bytes in our approach) and the main idea of the RFD algorithm which is based on one kind of agent called drop that moves from source to destination only. This will eliminate the need of feedback packets to update the network and offers a suitable solution to change data packet into smart packets. Simulation results shows improvement in the throughput and reduction in end to end delay and jitter compared to AODV and AntHocNet protocols.

A Hybrid Ant Colony Optimization Algorithm for Permutation Flow-Shop Scheduling in Manufacturing Systems and Industrial Process

This paper presents a novel hybrid ant colony optimization approach (ACO&VNS) to solve the permutation flow-shop scheduling problem (PFS) in manufacturing systems and industrial process. The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ant colony optimization (ACO) with variable neighborhood search (VNS) which can also be embedded into the ACO algorithm as neighborhood search to improve solutions. Moreover, the hybrid algorithm considers both solution diversification and solution quality. Finally, the experimental results for benchmark PFS instances have shown that the hybrid algorithm is very efficient to solve the permutation flow-shop scheduling in manufacturing engineering compared with the best existing methods in terms of solution quality.

A Hybrid Ant Colony Optimization Algorithm for Network Routing and Planning

The mechanism of route search problem is applied to various engineering fields. The various researches for the dynamic routing only revile the shortest path from source to destination but not vice versa. Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behavior is avoided. Several approaches have been integrated with ACO to improve its performance for DOPs. The main objective of this work is to search out the least-time-cost route in a variable-edge-weight graph. It can find multiple transmission paths from sources to the goal by parallel search. We introduce time-dependent pheromones and feedback path model as two heuristic factors to improve the basic ACO. Finally, this proposed heuristic algorithm is verified to be steady-going by repeated testing [1]. Ant Colony Optimization Technique has been applied in different network models with different number of nodes and structure to find the shortest path with optimum throughput. The simulation results show that the proposed dynamic ACO algorithm can effectively reduce time cost by avoiding the dynamic congestion areas. The experimental results show that the algorithm is more effective than the existing ones and it improves the Quality of Service (QoS) [13[14][15]. General Terms: Route Optimization, Shortest path, Realtime

A New Hybrid Ant Colony Optimization Algorithm for Permutation Flow-Shop Scheduling

This paper presents a novel hybrid ant colony optimization approach (ACO&PR) to solve the permutation flow-shop scheduling (PFS). The main feature of this hybrid algorithm is to hybridize the solution construction mechanism of the ACO with path relinking (PR), an evolutionary method, which introduces progressively attributes of the guiding solution into the initial solution to obtain the high quality solution. Moreover, the hybrid algorithm considers both solution diversification and solution quality, and it adopts the dynamic updating strategy of the reference set to accelerate the convergence towards high-quality regions of the search space. Finally, the experimental results for benchmark PFS instances have shown that our proposed method is very efficient to solve the permutation flow-shop scheduling compared with the best existing methods in terms of solution quality.

Hybrid ant colony optimization algorithms for mixed discrete–continuous optimization problems

This paper presents three new hybrid ant colony optimization algorithms that are extended from the ACOR developed by Socha and Dorigo for solving mixed discrete–continuous constrained optimization problems. The first two hybrids, labeled ACOR-HJ and ACOR-DE, differs in philosophy with the former integrating ACOR with the effective Hooke and Jeeves local search method and the latter a cooperative hybrid between ACOR and differentia evolution. The third hybrid, labeled ACOR-DE-HJ, is the second cooperative hybrid enhanced with the Hooke and Jeeves local search. All three algorithms incorporate a method to handle mixed discrete–continuous variables and the Deb’s parameterless penalty method for handling constraints. Fourteen problems selected from various domains were used for testing the performance of both algorithms. It was showed that all three algorithms greatly outperform the original ACOR in finding the exact or near global optima. An investigation was also carried out to determine the relative performance of applying local search with a fixed probability or varying probability.

Hybrid metaheuristic algorithms for minimum weight dominating set

Minimum weight dominating set (MWDS) finds many uses in solving problems as varied as clustering in wireless networks, multi-document summarization in information retrieval and so on. It is proven to be NP-hard, even for unit disk graphs. Many centralized and distributed, greedy and approximation algorithms have been proposed for the MWDS problem. However, all the approximation algorithms are limited to unit disk graphs which are primarily used to model wireless networks. This assumption fails when applied to other domains. In this paper, we present two metaheuristic algorithms – a hybrid genetic algorithm and a hybrid ant colony optimization algorithm – for the problem of computing minimum weight dominating set. We compare our results with that of a greedy heuristic as well as the only other metaheuristic algorithm proposed so far in the literature and show that our algorithms are far better than these algorithms.

New heuristics for two bounded-degree spanning tree problems

A vertex v of a connected graph G=(V,E) is called a branch vertex if its degree is greater than two. Pertaining to branch vertices, this paper studies two optimization problems having roots in the domain of optical networks. The first one, referred to as MBV, seeks a spanning tree T of G with the minimum number of branch vertices, whereas the second problem, referred to as MDS, seeks a spanning tree T of G with the minimum degree sum of branch vertices. Both MBV and MDS are NP-Hard. Two heuristics approaches are presented for each problem. The first approach is a problem specific heuristic, whereas the latter one is a hybrid ant-colony optimization algorithm. Computational results show the effectiveness of our proposed approaches.

A hybrid ant colony optimization algorithm for optimal multiuser detection in DS-UWB system

A hybrid ant colony optimization algorithm is proposed by introducing extremal optimization local-search algorithm to the ant colony optimization (ACO) algorithm, and is applied to multiuser detection in direct sequence ultra wideband (DS-UWB) communication system in this paper. ACO algorithms have already successfully been applied to combinatorial optimization; however, as the pheromone accumulates, we may not get a global optimum because it can get stuck in a local minimum resulting in a bad steady state. Extremal optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of optimization problems. Hence in this paper, a hybrid ACO algorithm, named ACO–EO algorithm, is proposed by introducing EO to ACO to improve the local-search ability of the algorithm. The ACO–EO algorithm is applied to multiuser detection in DS-UWB communication system, and via computer simulations it is shown that the proposed hybrid ACO algorithm has much better performance than other ACO algorithms and even equal to the optimal multiuser detector.

A new hybrid ant colony optimization algorithm for feature selection

In this paper, we propose a new hybrid ant colony optimization (ACO) algorithm for feature selection (FS), called ACOFS, using a neural network. A key aspect of this algorithm is the selection of a subset of salient features of reduced size. ACOFS uses a hybrid search technique that combines the advantages of wrapper and filter approaches. In order to facilitate such a hybrid search, we designed new sets of rules for pheromone update and heuristic information measurement. On the other hand, the ants are guided in correct directions while constructing graph (subset) paths using a bounded scheme in each and every step in the algorithm. The above combinations ultimately not only provide an effective balance between exploration and exploitation of ants in the search, but also intensify the global search capability of ACO for a high-quality solution in FS. We evaluate the performance of ACOFS on eight benchmark classification datasets and one gene expression dataset, which have dimensions varying from 9 to 2000. Extensive experiments were conducted to ascertain how AOCFS works in FS tasks. We also compared the performance of ACOFS with the results obtained from seven existing well-known FS algorithms. The comparison details show that ACOFS has a remarkable ability to generate reduced-size subsets of salient features while yielding significant classification accuracy.

Makespan minimization of a flowshop sequence-dependent group scheduling problem

The flowshop sequence dependent group sched- uling problem with minimization of makespan as the objective (Fm|fmls, Splk, prmu|Cmax) is considered in this paper. It is assumed that several groups with different number of jobs are assigned to a flow shop cell that has m machines. The goal is to find the best sequence of processing the jobs in each group and the groups themselves with minimization of makespan as the objec- tive. A mathematical model for the research problem is developed in this paper. As the research problem is shown to be NP-hard, a hybrid ant colony optimization (HACO) algorithm is developed to solve the problem. A lower bounding technique based on relaxing a few constraints of the mathematical model developed for the original problem is proposed to evaluate the quality of the HACO algorithm. Three different problem structures, with two, three, and six machines, are used in the generation of the test problems to test the performance of the algorithm and the lower bounding technique developed. The results obtained from the HACO algorithm and those that have appeared in the published literature are also compared. The comparative results show that the HACO algorithm has a superior performance compared to the best available algorithm based on memetic algorithm with an average percentage deviation of around 1.0% from the lower bound.

A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem

The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.