In this paper, a collaborative scheduling of discrete manufacturing logistics (CSDML) model is established for a single factory with multiple customers, considering multi-vehicle, delay, time window and capacity const...
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In this paper, a collaborative scheduling of discrete manufacturing logistics (CSDML) model is established for a single factory with multiple customers, considering multi-vehicle, delay, time window and capacity constraints. Based on the basic principle of batalgorithm, discrete bat algorithm (DBA) is proposed to solve the CSDML problem. A coding and decoding scheme is proposed to map the continuous domain to the discrete domain, the objective function is defined, and a local search strategy is adopted to enhance the search effect of the algorithm. Compared with DBA with random search and DBA without search, the proposed algorithm can get better experimental results.
The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot sp...
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The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot spot. Although conventional centrality-based and greedy-based methods show high performance, they are not very efficient when dealing with large-scale social networks. Recently, algorithms based on swarm intelligence are applied to solve the problems mentioned above, and the existing researches show that such algorithms can obtain the optimal global solution. In particular, the discrete bat algorithm (DBA) has been proved to have excellent performance, but the evolution mechanism based on a random selection strategy leads to the optimal solution's instability. To solve this problem, in this paper, we propose a clique-DBA algorithm. The proposed algorithm is based on the clique partition of a network and enhances the initial DBA algorithm's stability. The experimental results show that the proposed clique-DBA algorithm converges to a determined local influence estimation (LIE) value in each run, eliminating the phenomenon of large fluctuation of LIE fitness value generated by the original DBA algorithm. Finally, the simulated results achieved under the independent cascade model show that the clique-DBA algorithm has a comparable performance of influence spreading compared with the algorithms proposed in the state of the art.
Many public figures, companies and associations are planning events in different cities and at the same time have active profiles on social media. The planning process requires processing a large amount of data and di...
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ISBN:
(纸本)9789532330991
Many public figures, companies and associations are planning events in different cities and at the same time have active profiles on social media. The planning process requires processing a large amount of data and different parameters when choosing the best event venue. Social media captures a large number of fan actions per day. This paper describes the process of selecting the most appropriate cities to organize events, aided by data collected from social media. The problem is defined as a combinatorial optimization problem. A modified metaheuristic batalgorithm was proposed, implemented, and described in detail to solve the problem. Although the original batalgorithm is designed to solve continuous optimization problems, the implemented batalgorithm is adapted to solve the defined problem. The algorithm is compared to the exhaustive search method for smaller instances, and to the greedy and genetic algorithm for larger instances. The algorithm was tested on benchmark data on cities in 20 European countries, as well as on real data collected from pages on the social network Facebook. batalgorithm has shown superior results compared to other techniques, both in time and in the quality of the solutions generated.
Influence maximization aims to select a small set of k influential nodes to maximize the spread of influence. It is still an open research topic to develop effective and efficient algorithms for the optimization probl...
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Influence maximization aims to select a small set of k influential nodes to maximize the spread of influence. It is still an open research topic to develop effective and efficient algorithms for the optimization problem. Greedy-based algorithms utilize the property of "submodularity" to provide performance guarantee, but the computational cost is unbearable especially in large-scale networks. Meanwhile, conventional topology-based centrality methods always fail to provide satisfying identification of influential nodes. To identify the k influential nodes effectively, we propose a metaheuristic discrete bat algorithm (DBA) based on the collective intelligence of bat population in this paper. According to the evolutionary rules of the original batalgorithm (BA), a probabilistic greedy-based local search strategy based on network topology is presented and a CandidatesPool is generated according to the contribution of each node to the network topology to enhance the exploitation operation of DBA. The experimental results and statistic tests on five real-world social networks and a synthetic network under independent cascade model demonstrate that DBA outperforms other two metaheuristics and the Stop-and-Stair algorithm, and achieves competitive influence spread to CELF (Cost-Effective Lazy Forward) but has less time computation than CELF.
batalgorithm is an effective swarm intelligence optimization algorithm which is widely used to solve continuous optimization problems. But it still has some limitations in search process and can't solve discrete ...
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batalgorithm is an effective swarm intelligence optimization algorithm which is widely used to solve continuous optimization problems. But it still has some limitations in search process and can't solve discrete optimization problems directly. Therefore, this paper introduces an unordered pair and proposes an unordered pair batalgorithm (UPBA) to make it more suitable for solving symmetric discrete traveling salesman problems. To verify the effectiveness of this method, the algorithm has been tested on 23 symmetric benchmarks and compared its performance with other algorithms. The results have shown that the proposed UPBA outperforms all the other alternatives significantly in most cases.
A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a ser...
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A majority of existing works dealing with redundancy allocation problems are based on traditional series-parallel structures. While in many real-life scenarios, the way of connecting subsystems is not limited to a series-only configuration. This paper considers a generalized redundancy allocation problem (GRAP), where the system structure is a more general network. Since the reliability evaluation in GRAPs is a NP-hard problem and the traditional exact symbolic reliability calculation is not suitable, a cellular automata based monte carlo simulation method is implemented in this paper to estimate the system reliability. It is a relatively simple but effective method without knowing the MPs/MCs. Moreover, to deal with GRAPs, a novel discrete bat algorithm is proposed in this paper with a goal of determining an optimal system structure that achieves the minimum cost under several constraints by using redundant components in parallel. Computational complexity of the proposed algorithm is also calculated in this paper. In the end, three experiments are carried out based on ten networks to set parameters, measure the effectiveness of the modifications, and compare with other state-of-the-art algorithms, separately. The reported computational results show that the proposed algorithm is powerful, which is more superior on this sort of problems.
batalgorithm (BA) is one of the most recent bioinspired algorithm. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, we try t...
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ISBN:
(纸本)9781479946471
batalgorithm (BA) is one of the most recent bioinspired algorithm. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, we try to solve the graph coloring problem using a binary batalgorithm. To show the feasibility and the effectiveness of the algorithm, we have used the DIMACS benchmark, and the obtained results are very encouraging.
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