For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be ...powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.
On-line health status monitoring, a key part of prognostics and health management, provides various benefits, such as preventing unexpected failure and improving safety and reliability. In this ...paper, a data-driven approach for health status assessment is presented. A novel method based on discriminative deep belief networks (DDBN) and ant colony optimization (ACO) is used to predict health status of machine. DDBN is a new paradigm that utilizes a deep architecture to combine the advantages of deep belief networks and discriminative ability of back-propagation strategy. DDBN works through a greedy layer-by-layer training with multiple stacked restricted Boltzmann machines, which preserves information well when embedding features from high-dimensional space to low-dimensional space. However, selecting the parameters of DDBN is quite challenging. To address the problem, ACO is introduced to DDBN in this paper. By optimization, the structure of DDBN model is determined automatically without prior knowledge and the performance is enhanced. To evaluate the proposed approach, two case studies were carried out, which shows that it can achieve a good result. The performance of this model is also compared with support vector machine. It is concluded that the proposed method is very promising in the field of prognostics.
Path planning for multiple Unmanned Ground Vehicles (UGVs) is a critical problem for UGV autonomy and is increasingly attracting attention due to its wide applications. This paper presents a ...continuous ant colony-based multi-UGV path planner, which consists of UGV path planning and multi-UGV coordination. A continuous Ant Colony Optimisation with a Probability-based random-walk strategy and an Adaptive waypoints-repair method (ACOPAR) is proposed to optimise the path for each UGV. Collision avoidance among the UGVs for the multi-agent coordination problem is then resolved via a velocity shifting optimisation algorithm. In ACOPAR, exploration and exploitation are balanced using a probability-based random-walk strategy switching between a Brownian and a Cauchy motion to modify the construction process of new solutions. An adaptive waypoints-repair strategy and a re-initialisation strategy are designed to improve the algorithm’s performance in finding feasible paths. A test suite of multi-UGV path planning with 12 cases is proposed to evaluate the search capability and scalability of the proposed ACOPAR compared to other algorithms. Experimental results validate the superiority of ACOPAR, especially when solving complex, high-dimensional problems.
•A new ACOPARis designed for optimising the path for each UGV.•A new random-walk strategy switching between Brownian and Cauchy motion is designed.•Adaptive waypoints-repair-strategy to improve search accuracy and scalability.•Multi-agent coordination is designed to avoid the collision among UGVs.•Experiments validate the superiority of ACOPAR, especially on complex problems.
•In terms of improving fairness for the farmers to study a consortium in agricultural supply chain systems.•Utilize cyber physical systems and blockchain technology to construct a robust and ...trustable consortium.•Trustability, scalability, and share amount assignment in consortium have been addressed.
Trading aspect of agricultural supply chain system is sophisticated since it consists of many stages and involves various entities/agencies. Recently, blockchain technology could prove its effectiveness to solve some of the concerns in agricultural supply chain systems. Nevertheless, maximizing profit for producers (in our study farmers) is another important concern that can be addressed by consortium establishment which blockchain technology is the best solution for this purpose due to the following reasons. First, since all the nodes in the blockchain keep the verified and synchronized version of the chain, each node can verify the transactions’ transparency. Second, blockchain technology is temper-proof that means no one can change the history of the transactions. These two main features of blockchain technology can provide a suitable ground to construct a consortium among the producers. However, there are other specific requirements that a successful consortium in agricultural supply chain system should address them that motivate us to a new design of blockchain technology. More precisely, in our design we consider the problems of trustability, scalability, and share amount assignment. For trustability, we utilize cyber physical system to ensure the quantity and quality of the products. Scalability is being addressed by adopting the concept of public service platform and proposing a new consensus algorithm. And finally share amount assignment is being solved by our improved version of ant colony optimization algorithm. Experimental results and analysis prove the effectiveness and accuracy of our proposed design for blockchain technology.
Evolutionary algorithms and other meta-heuristics have been employed widely to solve optimization problems in many different fields over the past few decades. Their performance in finding optimal ...solutions often depends heavily on the parameterization of the algorithm's search operators, which affect an algorithm's balance between search diversification and intensification. While many parameter-adaptive algorithms have been developed to improve the searching ability of meta-heuristics, their performance is often unsatisfactory when applied to real-world problems. This is, at least in part, because available computational budgets are often constrained in such settings due to the long simulation times associated with objective function and/or constraint evaluation, thereby preventing convergence of existing parameter-adaptive algorithms. To this end, this paper proposes an innovative parameter-adaptive strategy for ant colony optimization (ACO) algorithms based on controlling the convergence trajectory in decision space to follow any prespecified path, aimed at finding the best possible solution within a given, and limited, computational budget. The utility of the proposed convergence trajectory controlled ACO (ACO CTC ) algorithm is demonstrated using six water distribution system design problems (WDSDPs, a difficult type of combinatorial problem in water resources) with varying complexity. The results show that the proposed ACO CTC successfully enables the specified convergence trajectories to be followed by automatically adjusting the algorithm's parameter values. Different convergence trajectories significantly affect the algorithm's final performance (solution quality). The trajectory with a slight bias toward diversification in the first half and more emphasis on intensification during the second half of the search exhibits substantially improved performance compared to the best available ACO variant with the best parameterization (no convergence control) for all WDSDPs and computational scenarios considered. For the two large-scale WDSDPs, new best-known solutions are found by the proposed ACO CTC .
The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed maximum power point (MPP) tracking (MPPT) schemes for photovoltaic (PV) ...power-generation systems. However, under partially shaded conditions (PSCs), P&O method miserably fails to recognize global MPP (GMPP) and gets trapped in one of the local MPPs (LMPPs). This paper proposes ant-colony-based search in the initial stages of tracking followed by P&O method. In such a hybrid approach, the global search ability of ant-colony optimization (ACO) and local search capability of P&O method are integrated to yield faster and efficient convergence. A theoretical analysis of the static and dynamic convergence behavior of the proposed algorithm is presented together with computed and measured results.
Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed sensor nodes connected through the wireless medium to monitor and record the physical information from the ...environment. The nodes of WSN are battery powered, so after a certain period it loose entire energy. This energy constraint affects the lifetime of the network. The objective of this study is to minimize the overall energy consumption and to maximize the network lifetime. At present, clustering and routing algorithms are widely used in WSNs to enhance the network lifetime. In this study, the Butterfly Optimization Algorithm (BOA) is employed to choose an optimal cluster head from a group of nodes. The cluster head selection is optimized by the residual energy of the nodes, distance to the neighbors, distance to the base station, node degree and node centrality. The route between the cluster head and the base station is identified by using Ant Colony Optimization (ACO), it selects the optimal route based on the distance, residual energy and node degree. The performance measures of this proposed methodology are analyzed in terms of alive nodes, dead nodes, energy consumption and data packets received by the BS. The outputs of the proposed methodology are compared with traditional approaches LEACH, DEEC and compared with some existing methods FUCHAR, CRHS, BERA, CPSO, ALOC and FLION. For example, the alive nodes of the proposed methodology are 200 at 1500 iterations which is higher compared to the CRHS and BERA methods.
Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow ...consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.
The blossoming of electric vehicles gives rise to a new vehicle routing problem (VRP) called capacitated electric VRP. Since charging is not as convenient as refueling, both the service of customers ...and the recharging of vehicles should be considered. In this article, we propose a confidence-based bilevel ant colony optimization (ACO) algorithm to solve the problem. It divides the whole problem into the upper level subproblem capacitated VRP and the lower level subproblem fixed routing vehicle charging problem. For the upper level subproblem, an ACO algorithm is used to generate customer service sequence. Both the direct encoding scheme and the order-first split-second encoding scheme are implemented to make a guideline of their applicable scenes. For the lower level subproblem, a new heuristic called simple enumeration is proposed to generate recharging schedules for vehicles. Between the two subproblems, a confidence-based selection method is proposed to select promising customer service sequence to conduct local search and lower level optimization. By setting adaptive confidence thresholds, the inferior service sequences that have little chance to become the iteration best are eliminated during the execution. The experiments show that the proposed algorithm has reached the state-of-the-art level and updated eight best known solutions of the benchmark.
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