Migrating Birds Optimization Algorithm Research Articles

Improved Migrating Birds Optimization Algorithm to Solve Hybrid Flowshop Scheduling Problem With Lot-Streaming

Hybrid flowshop scheduling problem with lot-streaming (HLFS) has played an important role in modern industrial systems. In this paper, we preset an improved migrating birds optimization (IMBO) algorithm for HLFS to minimize makespan. To ensure the diversity of initial population, a Nawaz-Enscore-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.

Energy-Efficient Integration Optimization of Production Scheduling and Ladle Dispatching in Steelmaking Plants

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.

A competitive and cooperative Migrating Birds Optimization algorithm for vary-sized batch splitting scheduling problem of flexible Job-Shop with setup time

Batch production has been widely used in mass-producing manufacturing where batch splitting techniques can be applied to improve the production efficiency. Therefore the Batch Splitting Scheduling Problem of Flexible Job-Shop arises, which is a complicated problem with complex constraints and structure. In this paper, the Migrating Birds Optimization algorithm is improved so that it can be applied to the problem and a Competitive and Cooperative Migrating Birds Optimization algorithm is proposed, which consists of three main stages. The improved V-shaped flying stage can avoid the neighborhood of the follower birds being wholly abandoned and prevent premature convergence. The adjustment stage can diversify the formation of the bird population and mitigate the loss of population diversity. The competition stage can improve the searching efficiency of the population. Through experiments on several test cases, the effectiveness of the three proposed stages is confirmed, and the proposed algorithm is proven to effectively decrease the makespan of Batch Splitting Scheduling Problem of Flexible Job-Shop.

On the end-of-life state oriented multi-objective disassembly line balancing problem

The biggest difference between a disassembly line and an assembly line is that there are many uncertainties in structure and quality of the disassembled products in a disassembly line. The disassembly line balancing problem, considering the effect of end-of-life states caused by the uncertainty of the structure or the quality of the disassembled products, is addressed in this paper. A multi-objective mathematical model for the addressed problem is built with three optimization goals: minimizing the number of workstations, minimizing the idle index and minimizing the number of resources. Then a multi-objective hybrid migrating birds optimization algorithm is proposed, which uses a greedy random search operation based on embedding mechanism to generate neighborhood individuals. To avoid the problem of easily being trapped into a local optimum by a basic migrating birds optimization algorithm, a reset mechanism based on simulated annealing operation is set up to accept other solutions with a certain probability, so that the algorithm can escape out of a local optimum. By solving disassembly examples of different scales in the literature and comparing with the existing algorithms, the effectiveness and superiority of the proposed multi-objective hybrid migrating birds optimization algorithm is validated. Finally, the proposed model and algorithm are applied to solving two disassembly instances, and the solving results are compared with the single-objective optimal solution solved by LINGO 11.0 solver and the basic migrating birds optimization algorithm to further identify the performance of the proposed algorithm.

Multi-Objective Migrating Birds Optimization Algorithm for Stochastic Lot-Streaming Flow Shop Scheduling With Blocking

Blocking lot-streaming flow shop scheduling problem with the stochastic processing time has a wide range of applications in various industrial systems. However, this problem has not yet been well studied. In this paper, the above-mentioned problem is transformed into a determinate multi-objective optimization one using the Monte Carlo sampling method. A Multi-Objective Migrating Birds Optimization (MOMBO) algorithm is then proposed to solve the above-mentioned re-formulated multi-objective scheduling problem, in which the multiple-based PFE is proposed to yield the initial solutions with high quality, the information of the non-dominated solutions is learned and sampled to improve the global searching ability of MOMBO, and a reference-point-assisted local search method for multi-objective optimization is applied to further enhance the exploitation capability of MOMBO. To evaluate the performance of the MOMBO, several comparative experiments are executed on 180 test scheduling instances. The experimental results demonstrate that the MOMBO outperforms the compared algorithms in convergence and distributivity and has capacities to tackle the uncertainties.

Mathematical models and migrating birds optimization for robotic U-shaped assembly line balancing problem

Modern assembly line systems utilize robotics to replace human resources to achieve higher level of automation and flexibility. This work studies the task assignment and robot allocation in a robotic U-shaped assembly line. Two new mixed-integer programming linear models are developed to minimize the cycle time when the number of workstations is fixed. Recently developed migrating birds optimization algorithm is employed and improved to solve large-sized problems. Problem-specific improvements are also developed to enhance the proposed algorithm including modified consecutive assignment procedure for robot allocation, iterative mechanism for cycle time update, new population update mechanism and diversity controlling mechanism. An extensive comparative study is carried out to test the performance of the proposed algorithm, where seven high-performing algorithms recently reported in the literature are re-implemented to tackle the considered problem. The computational results demonstrate that the developed models are capable to achieve the optimal solutions for small-sized problems, and the proposed algorithm with these proposed improvements achieves excellent performance and outperforms the compared ones.

Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

Worker assignment is a relatively new problem in assembly lines that typically is encountered in situations in which the workforce is heterogeneous. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). This problem is different from the well-known simple assembly line balancing problem concerning the task execution times, and it varies according to the assigned worker. Minimal work has been reported in worker assignment in two-sided assembly lines. This research studies worker assignment and line balancing in two-sided assembly lines with an objective of minimizing the cycle time (TALWABP). A mixed-integer programming model is developed, and CPLEX solver is used to solve the small-size problems. An improved migrating birds optimization algorithm is employed to deal with the large-size problems due to the NP-hard nature of the problem. The proposed algorithm utilizes a restart mechanism to avoid being trapped in the local optima. The solutions obtained using the proposed algorithms are compared with well-known metaheuristic algorithms such as artificial bee colony and simulated annealing. Comparative study and statistical analysis indicate that the proposed algorithm can achieve the optimal solutions for small-size problems, and it shows superior performance over benchmark algorithms for large-size problems.

A multi-objective migrating birds optimization algorithm for the hybrid flowshop rescheduling problem

In this paper, a multi-objective hybrid flowshop rescheduling problem with respect to the production efficiency and instability is addressed in a dynamic shop environment, where two types of real-time events are simultaneously considered, i.e., machine breakdown and job cancelation. To solve the problem, a multi-objective migrating birds optimization (MMBO) is proposed. In the proposed algorithm, each solution is evaluated based on the Pareto dominance relationship, and an improvement procedure is further designed to help improve the solutions quality. The fast non-dominated sorting technique is introduced to sequence the solutions in the V-shaped population. For the leader evolution, it is conducted by a Pareto-based local search method, and within the process two neighbors sets are produced to, respectively, participate in the followers evolution in the two lines. For the followers evolution, the reproduction process is introduced and the benefit mechanism is implemented by combing the genetic operators. And in the two lines, different genetic operators are employed to achieve their collaboration. For the leader change, only the promising solutions can be forwarded to the leader position. A shuffling process is proposed to help share evolutionary information between the two lines and promote their joint efforts. The performance of the MMBO is evaluated by comparing with several state-of-the-art evolutionary multi-objective optimizers, and the computational results demonstrate the effectiveness of the proposed algorithm.

Enhanced migrating birds optimization algorithm for U-shaped assembly line balancing problems with workers assignment

U-shaped assembly lines have been popularly adopted in electronics and appliances to improve their flexibility and efficiency. However, most past studies assumed that the processing time of each task is fixed and hence just considered the task allocation but ignored worker assignment. In this paper, the processing time of each task depends on the workers and then the cooperative optimization of task allocation and workers assignment is considered in U-shaped assembly line balancing problems to optimize the cycle time. Later, an enhanced migrating birds optimization algorithm (EMBO) is proposed to solve it. In the EMBO algorithm, since this new problem has two subproblems: task allocation and worker assignment, the prevent work designs two neighborhood structures to improve the leader and following birds. Furthermore, the temperature acceptance criteria, to judge whether the neighbor replaces current following bird, are developed to ensure the diversity of population and avoid being trapped in the local optimum. And a competitive mechanism is introduced to increase the probability of the promising birds locating in the front of the line. The proposed algorithm is compared with other well-known algorithms in the literature, and the numerical results demonstrate that the proposed algorithm outperforms other algorithms.

PSO-based improved multi-flocks migrating birds optimization (IMFMBO) algorithm for solution of discrete problems

In this paper, we proposed an improved migrating birds optimization algorithm to solve discrete problem. It is a metaheuristic search algorithm that is inspired by V formation during the migration of migratory birds. Proposed algorithm has two main modifications on basic migrating birds algorithm. Firstly, multi-flocks are used instead of single flock in order to avoid local minimum. Secondly, these flocks interact with each other for the more detailed search around flock that has got better solutions. This interaction is inspired by particle swarm optimization algorithm. Also, insertion method is used for neighborhood in migrating birds optimization algorithm. As a discrete problem, traveling salesman problem is chosen. Performance of the proposed algorithm is tested on some of symmetric benchmark problems from TSPLIB. Obtained results show that proposed method is superior to basic migrating birds algorithm.

A Comparative Analysis of Metaheuristic Approaches for Multidimensional Two-Way Number Partitioning Problem

In this study, a novel usage of four metaheuristic approaches Genetic algorithm (GA), Simulated annealing (SA), migrating bird optimization algorithm (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.

Enhanced migrating birds optimization algorithm for the permutation flow shop problem with sequence dependent setup times

This paper presents an enhanced migrating bird optimization (MBO) algorithm and a new heuristic for solving a scheduling problem. The proposed approaches are applied to a permutation flowshop with sequence dependent setup times and the objective of minimizing the makespan. In order to augment the MBOs intensification capacity, an original problem specific heuristic is introduced. An adapted neighborhood, a tabu list, a restart mechanism and an original process for selecting a new leader also improved the MBO’s behavior. Using benchmarks from the literature, the resulting enhanced MBO (EMBO) gives state-of-the-art results when compared with other algorithms reference. A statistical analysis of the numerical experiments confirms the relative efficiency and effectiveness of both EMBO and the new heuristic.

A Variable Neighborhood Migrating Birds Optimization Algorithm for Flexible Job Shop Scheduling

A Variable Neighborhood Migrating Birds Optimization Algorithm for Flexible Job Shop Scheduling

Migrating birds optimization (MBO) algorithm to solve knapsack problem

This study presents Migrating Birds Optimization (MBO) which is a novel meta-heuristic algorithm for the solution of knapsack problem. The knapsack problem which is classified as NP-complete problem is a combinatorial optimization problem. Its aim is to achieve maximum benefit without exceeding the capacity of the knapsack with selected item. The Migrating Birds Algorithm is designed for discrete problems. Therefore, the performance of basic the MBO algorithm is tested on the some knapsack problems and obtained results are demonstrated in detail.

System identification by using migrating birds optimization algorithm: a comparative performance analysis

System identification is an important process to investigate and understand the behavior of an unknown system. It aims to establish an interface between the real system and its mathematical representation. Conventional system identification methods generally need differentiable search spaces and they cannot be used for nondifferentiable multimodal search spaces. On the other hand, metaheuristic search algorithms are independent from the search space characteristics and they do not need much knowledge about the real system. The migrating birds optimization algorithm is a recently introduced nature-inspired metaheuristic neighborhood search approach. It simulates the V flight formation of migrating birds, which enables birds to save energy during migration. In this paper, first, a set of comparative performance tests by using benchmark functions are performed on the migrating birds optimization algorithm and some other well-known metaheuristics. The same metaheuristic algorithms are then employed to solve several system identification problems. The results show that the migrating birds optimization algorithm achieves promising optimizations both for benchmark tests and for system identification problems.

Balancing exploration and exploitation by using sequential execution cooperation between artificial bee colony and migrating birds optimization algorithms

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 migrating birds optimization (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 optimization algorithms 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.

Experimental Model for Teaching Software Testing

The topic of software testing in education has become appealing to some although most researchers focused on researching software testing tools and methods. This is because of the increase awareness of the importance of adding software testing to the curricula in universities. In this paper, we propose the concept of teaching software testing with an experimental model for pairwise testing; developed with Migrating Birds Optimization (MBO) algorithm called Experimental Model for Teaching Software Testing (EMTST). The EMTST framework is explained in details and along with the comparison of MBO algorithm with other pairwise strategies. In conclusion, this strategy with MBO can help students understand the concept and practical aspect of the development of a pairwise strategy and how to evaluate the effectiveness of the algorithm in AI-based pairwise strategy.

Modified migrating birds optimization algorithm for closed loop layout with exact distances in flexible manufacturing systems

This paper addresses a new meta-heuristic algorithm to solve a closed loop layout problem. The proposed algorithm applies a modified version of the recently invented migrating birds optimization method. The computational experiments show that in most of the benchmark problems the results obtained from the proposed migrating birds optimization method is better than those obtained by other methods which are published in the literature.

A Modified Migrating Bird Optimization For University Course Timetabling Problem

University course timetabling problem is a dilemma which educational institutions are facing due to various demands to be achieved in limited resources. Migrating bird optimization (MBO) algorithm is a new meta-heuristic algorithm which is inspired by flying formation of migrating birds. It has been applied successfully in tackling quadratic assignment problem and credit cards fraud detection problem. However, it was reported that MBO will get stuck in local optima easily. Therefore, a modified migrating bird optimization algorithm is proposed to solve post enrolment-based course timetabling. An improved neighbourhood sharing mechanism is used with the aim of escaping from local optima. Besides that, iterated local search is selected to be hybridized with the migrating bird optimization in order to further enhance its exploitation ability. The proposed method was tested using Socha’s benchmark datasets. The experimental results show that the proposed method outperformed the basic MBO and it is capable of producing comparable results as compared with existing methods that have been presented in literature. Indeed, the proposed method is capable of addressing university course timetabling problem and promising results were obtained.

Migrating Birds Optimization for Flow Shop Sequencing Problem

FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.