Hybrid flowshop scheduling problem with lot-streaming (HLFS) has played an important role in modern industrial systems. In this paper, we preset an improved migratingbirdsoptimization (IMBO) algorithm for HLFS to mi...
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Hybrid flowshop scheduling problem with lot-streaming (HLFS) has played an important role in modern industrial systems. In this paper, we preset an improved migratingbirdsoptimization (IMBO) algorithm for HLFS to minimize makespan. To ensure the diversity of initial population, a Nawaz & x2013;Enscore & x2013;Ham (NEH) heuristic algorithm is used to generate the leader, and the remaining solutions are randomly generated. According to the characteristics of the HLFS problem, we propose a combined neighborhood search structure that consists of four different neighborhood operators. We design effective local search procedure to explore potential promising domains. In addition, a reset mechanism is added to avoid falling into local optimum. Extensive experiments and comparison demonstrate the feasibility and effectiveness of the proposed algorithm.
The existing production scheduling mode ignores ladle dispatching resulting in the increase of energy consumption in ladle heating and instability in production. Hence, we study the energy-efficient integration optimi...
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The existing production scheduling mode ignores ladle dispatching resulting in the increase of energy consumption in ladle heating and instability in production. Hence, we study the energy-efficient integration optimization of production scheduling and ladle dispatching in this paper. Specifically, a mixed integer linear programming model is formulated to coordinate the time-dependent correlations between them and quantify the energy consumption of them. Moreover, an enhanced migrating birds optimization algorithm (EMBO) is proposed to tackle this NP-hard integration optimization problem. In this proposed algorithm, a three-level rule-based heuristic decoding is designed to achieve the optimal solutions at the given production sequence;well-designed neighborhood structures are appended to intensify exploration;a simulated annealing-based acceptance criterion is hired to escape from local optima. Additionally, a novel competitive mechanism for birds regrouping is developed to increase the population diversity by information exchange between the left and right lines of V-formation. Mass experimental results demonstrate that the proposed EMBO observably outperforms all the compared algorithms, and the proposed integration optimization decreases the energy-consumption by 1.21% in the context of constant production efficiency.
The artificial bee colony (ABC) algorithm is a metaheuristic search method inspired by bees' foraging behaviour. With its global search ability in scout bee phase, it can easily escape from local optimum traps in ...
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The artificial bee colony (ABC) algorithm is a metaheuristic search method inspired by bees' foraging behaviour. With its global search ability in scout bee phase, it can easily escape from local optimum traps in the problem space. Therefore, it is good at exploration. The migratingbirdsoptimization (MBO) algorithm is another recent metaheuristic search method. It simulates birds' V flight formation, which minimizes energy consumption during flight. The MBO algorithm achieves a good convergence to the global optimum by using its own unique benefit mechanism. That is, it has a good exploitation capability. This paper aimed to combine the good exploration property of the ABC algorithm and the good exploitation property of the MBO algorithm via a sequential execution strategy. In the proposed method, firstly, the ABC algorithm runs. This enables solutions to escape from local optimum traps and orientates them to the region in which the global optimum exists. Then the MBO algorithm runs. It performs a good convergence to the global optimum. In the proposed method, some variants of the ABC algorithm and some other well-known optimizationalgorithms were tested via benchmark functions. It was seen in the experiments that the proposed method gave competitive benchmark test results considering both success rates and convergence performances.
In this study, a novel usage of four metaheuristic approaches Genetic algorithm (GA), Simulated annealing (SA), migrating bird optimizationalgorithm (MBO) and clonal selection algorithm (CSA) are applied to multidime...
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In this study, a novel usage of four metaheuristic approaches Genetic algorithm (GA), Simulated annealing (SA), migrating bird optimizationalgorithm (MBO) and clonal selection algorithm (CSA) are applied to multidimensional two-way number partitioning problem (MDTWNPP). MDTWNPP is a classical combinatorial NP-hard optimization problem where a set of vectors have more than one coordinate is partitioned into two subsets. The main objective function of MDTWNPP is to minimize the maximum absolute difference between the sums per coordinate of elements. In order to solve this problem, GA is applied with greedy crossover and mutation operators. SA is improved with dual local search mechanism. MBO is specialized as multiple flock migrating birds optimization algorithms. CSA is applied with problem specific hyper mutation process. Furthermore, all instances are solved using an integer linear programming model which was previously presented in the literature. In the experiments, four metaheuristic approaches and integer linear programming model are used to solve 126 datasets with different sizes and coordinates. As a brief result, the GA and SA approaches designed for this problem outperformed all other heuristics and the integer programming model. Both the performance of GA and SA approaches are in a competitive manner where GA and SA yielded the best solution for 56 and 65 out of 125 datasets, respectively.
Statistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification proble...
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ISBN:
(纸本)9783642386817
Statistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification problem the costs of the two types of classification errors (FP=false positive and FN=false negative) are not the same. Thus, the classical data mining algorithms do not fit to the problem exactly. Departing from this fact, we have solved this problem by genetic algorithms and scatter search. Now, we apply the recently developed new metaheuristics algorithm namely the migrating birds optimization algorithm (MBO) to this problem. Results show that it outperforms the former approach. The performance of standard MBO is further increased by the help of some modified benefit mechanisms.
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