Migrating Birds Optimization Algorithm Research Articles

Enhanced migrating birds optimization algorithm for optimization problems in different domains

AbstractMigrating birds optimization algorithm is a promising metaheuristic algorithm recently introduced to the optimization community. In this study, we propose a superior version of the migrating birds optimization algorithm by hybridizing it with the simulated annealing algorithm which is one of the most popular metaheuristics. The new algorithm, called MBOx, is compared with the original migrating birds optimization and four well-known metaheuristics, including the simulated annealing, differential evolution, genetic algorithm and recently proposed harris hawks optimization algorithm. The extensive experiments are conducted on problem instances from both discrete and continuous domains; feature selection problem, obstacle neutralization problem, quadratic assignment problem and continuous functions. On problems from discrete domain, MBOx outperforms the original MBO and others by up to 20.99%. On the continuous functions, it is observed that MBOx does not lead the competition but takes the second position. As a result, MBOx provides a significant performance improvement and therefore, it is a promising solver for computational optimization problems.

A simple migrating birds optimization algorithm with two search modes to solve the no-wait job shop problem

The no-wait job shop problem has important applications in various industries such as steel-making and chemical processes, and its mainstream approaches are metaheuristics with timetabling methods. Inspired by the characteristics of the problem, we consider both left and right shifts for some jobs and present an effective timetabling method called left–right shift (LRS) method. The LRS method schedules jobs as far left as possible and tries all the possible right shifts for some jobs to expand the search space. We propose a simple migrating birds optimization (SMBO) algorithm for searching the space of job sequences. The population of the proposed algorithm is equally divided into two subpopulations with two search modes, one for the original instance and the other for the inverse instance. Local exploitation and global exploration are well balanced in the population evolution scheme of the proposed algorithm. To enhance the run efficiency of the algorithm, we introduce a speedup method for left timetabling and employ the simultaneous multithreading technique for the parallel evolution of subpopulations. Experimental results show that the proposed SMBO algorithm outperforms the state-of-the-art by a significant margin and obtains new upper bounds for a large number of benchmark instances.

A multi-objective migrating birds optimization algorithm based on game theory for dynamic flexible job shop scheduling problem

A multi-objective migrating birds optimization algorithm based on game theory for dynamic flexible job shop scheduling problem

Solving a new application of asymmetric TSP by modified migrating birds optimization algorithm

Solving a new application of asymmetric TSP by modified migrating birds optimization algorithm

A co-evolutionary migrating birds optimization algorithm based on online learning policy gradient

A co-evolutionary migrating birds optimization algorithm based on online learning policy gradient (CMBO-PG) is proposed to address complex continuous real-parameter optimization problems. In CMBO-PG, a Gaussian estimation of distribution algorithm (GEDA), which enhances the exploitation tendency, is utilized to generate the solutions of the leading flock. The neighborhood solutions of the following flock are produced by a multi-strategy learning mechanism to promote exploration capability. The co-evolution of the leading flock and following flock is realized by the information-sharing mechanism and the operation of destruction and construction to keep the balance of exploration and exploitation. The nonlinear selection of mutation strategies is laborious due to the differences in the ability to address optimization problems. In the mechanism of multi-strategy learning, a long short-term memory (LSTM) is adopted as a selector of mutation strategies to predict the selection probability of three mutation strategies. The evolutionary procedure of the following flock is modeled as a Markov decision process (MDP). The policy gradient (PG) is employed as a model optimizer to control the parameters of LSTM based on the historical feedback information. The performance of CMBO-PG is testified on the CEC 2017 benchmark test suite. The experimental results show that CMBO-PG is superior to the 12 comparison algorithms, including state-of-art algorithms.

Balancing and sequencing of mixed-model assembly line considering preventive maintenance scenarios: mathematical model and a migrating birds optimization algorithm

Balancing and sequencing of mixed-model assembly line considering preventive maintenance scenarios: mathematical model and a migrating birds optimization algorithm

Multi-manned assembly line balancing with sequence-dependent set-up times using an enhanced migrating birds optimization algorithm

In multi-manned assembly lines, more than one operator is allowed on the same workstation. Limited research has been carried out on sequence-dependent set-up times. Therefore, this article optimizes the multi-manned assembly line balancing problem with sequence-dependent set-up times using an enhanced migrating birds optimization (EMBO) algorithm. Sequence-dependent set-up times are embedded in two ways: forward and backward set-ups. EMBO designs a mixed neighbourhood search and acceptance criteria to help it escape from local optima, and a diversification mechanism to enhance the global exploration ability. Three types of experiment and two scenarios, with low and high set-up times, are conducted to test the performance of the mixed-integer linear programming (MILP) model and EMBO. The first experiment compares three MILP models (no, low and high set-up times) to illustrate the significance of set-up times. The final two experiments demonstrate the effectiveness of EMBO by comparison with the MILP model and six state-of-the-art methods.

Biological inspired self‐organized secure autonomous routing protocol and secured data assured routing in WSN: Hybrid EHO and MBO approach

SummaryNowadays, the telecommunication system has attained remarkable growth, especially wireless sensor network (WSN) based on the development of various electronic devices. WSN signals are not so much solid. Here, the routers are utilized to work appropriately. Wireless networks have low expenses and are simple to install. But the major challenge concerns with providing secure data transmission via the wireless network, because the data are attacked by the hackers and also the parts of information can be stolen during transmission. In this manuscript, a biological inspired self‐organized secure autonomous routing protocol (BIOSARP) and secured data assured routing (SDAR) in WSN by means of combined elephant herding optimization (EHO) and migrating birds optimization (MBO) approach is proposed to the dynamic security of data via WSN. The BIOSARP by means of Clan updating operator in EHO so as to locate the optimal route and SDAR is advanced by updating the trust level to locate a trustworthy neighbour by means of path discovery approach of MBO algorithm. The proposed approach is executed in network simulator 2 (NS‐2) software. The experimental outcomes are analyzed using some performance evaluating metrics like delivery ratio (DR), energy consumption (EC), packet overhead (PO), and accuracy. Finally, the experimental outcomes demonstrate that the proposed approach outperforms other conventional approaches like BIOSARP‐SRTLD and BIOSARP by providing better packet DR, EC, PO, and accuracy.

Modified Migrating Birds Optimization Algorithm: Multi-Depot Capacitated Vehicle Routing Problem

The multi-depot capacitated vehicle routing problem (MDCVRP) is a variation of the vehicle routing problem (VRP) modeled from distribution problems in the industrial world. This problem is a complex optimization problem in the field of operations research in applied mathematics. The MDCVRP is very interesting to discuss and find the best solution method. In this study, the authors apply the modified migrating birds' optimization (MMBO) algorithm, which is a hybrid of the migrating birds' optimization (MMBO) and iterated local search (ILS) algorithms. The purpose of this study is to analyze the results of applying the algorithm in solving MDCVRP. We used 20 MDCVRP data in simulation, grouped into four sizes (25, 50, 75, and 100 points). Based on the results of this research, it is known that the MMBO algorithm can produce the following solutions. First, on the data of 25 points, the experiment reaches the optimal value with small convergent iterations. Second, the best results on the data of 50 points have reached optimal value, but some other results have not been optimal. And, third, for data of 75 and 100 points, there is no optimal solution obtained by the MMBO algorithm. These results conclude that the MMBO algorithm effectively solves the MDCVRP problem with small data, but the bigger data, the more ineffective.Keywords: MDCVRP; VRP; optimization; operation research; applied Mathematics; MMBO. AbstrakMulti-depot capacitated vehicle routing problem (MDCVRP) adalah salah satu variasi dari vehicle routing problem (VRP) yang dimodelkan dari permasalahan distribusi di dunia industri. Permasalahan ini merupakan permasalahan optimasi kompleks dalam bidang riset operasi ilmu matematika terapan. MDCVRP sangat menarik untuk dibahas dan dicari metode penyelesaian terbaik. Dalam penelitian ini, penulis menerapkan algoritma modified migrating birds optimization (MMBO) yang merupakan hybrid algoritma migrating birds optimization (MBO) dan iterated local search (ILS). Tujuan penelitian ini adalah menganalisis hasil penerapan algoritma dalam menyelesaikan MDCVRP. Untuk simulasi, penulis menggunakan 20 data MDCVRP yang dikelompokkan menjadi empat ukuran (25, 50, 75, dan 100 titik). Berdasarkan hasil penelitian yang telah dilakukan, diketahui bahwa algoritma MMBO mampu menghasilkan solusi sebagai berikut. Pertama, Pada data 25 titik, percobaan mencapai nilai optimal dengan iterasi konvergen yang kecil. Kedua, Hasil terbaik pada data 50 titik telah mencapai nilai optimal namun sebagain hasil lainnya belum optimal. Dan ketiga, untuk data 75 dan 100 titik, tidak terdapat solusi optimal yang dihasilkan algoritma MMBO. Dari hasil tersebut dapat disimpulkan bahwa algoritma MMBO efektif untuk menyelesaikan MDCVRP data kecil, namun semakin besar datanya menjadi kurang efektif.Kata kunci: MDCVRP; VRP; optimasi; riset operasi; matematika terapan; MMBO.

Hyperheuristic Based Migrating Birds Optimization Algorithm for a Fairness Oriented Shift Scheduling Problem

The purpose of this paper is twofold. First, it introduces a new hybrid computational intelligence algorithm to the optimization community. This novel hybrid algorithm has hyperheuristic (HH) neighborhood search movements embedded into a recently introduced migrating birds optimization (MBO) algorithm. Therefore, it is called HHMBO. Second, it gives the necessary mathematical model for a shift scheduling problem of a manufacturing company defined by including the fairness perspective, which is typically ignored especially in manufacturing industry. Therefore, we call this complex optimization problem fairness oriented integrated shift scheduling problem (FOSSP). HHMBO is applied on FOSSP and is compared with the well-known simulated annealing, hyperheuristics, and classical MBO algorithms through extended computational experiments on several synthetic datasets. Experiments demonstrate that the new hybrid computational intelligence algorithm is promising especially for large sized instances of the specific problem defined here. HHMBO has a high exploration capability and is a promising technique for all optimization problems. To justify this assertion, we applied HHMBO to the well-known quadratic assignment problem (QAP) instances from the QAPLIB. HHMBO was up to 14.6% better than MBO on converging to the best known solutions for QAP benchmark instances with different densities. We believe that the novel hybrid method and the fairness oriented model presented in this study will give new insights to the decision-makers in the industry as well as to the researchers from several disciplines.

Migrating birds optimization with a diversified mechanism for blocking flow shops to minimize idle and blocking time

Blocking flow shop scheduling problem widely exists in industrial processes, and most attention has focused on minimization of economic indicators, such as makespan, total flow time, and due-date-based functions, rather than energy-efficient indicators. This paper considers idle and blocking time criterion, which is closely related to machine energy consumption in blocking flow shops, and proposes a migrating birds optimization with a diversified mechanism (dMBO) for the problem. On the basis of the profile fitting (PF) heuristic and the characteristics of the idle and blocking time, an improved heuristic, named PFI, is proposed by modifying the PF and performing an insert procedure. A best insert operator and an insert-based local search are hybridized in the proposed migrating birds optimization algorithm to enhance its exploitation capability. In order to maintain the diversity of the flock in the algorithm, a diversified mechanism containing three tabu lists and one candidate pool is designed. Extensive computational results validate the effectiveness of the proposed PFI heuristic, and a statistical analysis of the computational results confirms the superiority of the dMBO over several other high-performing metaheuristics.

An efficient migrating birds optimization algorithm with idle time reduction for Type-I multi-manned assembly line balancing problem

An efficient migrating birds optimization algorithm with idle time reduction for Type-I multi-manned assembly line balancing problem

Multi-objective migrating bird optimization algorithm for cost-oriented assembly line balancing problem with collaborative robots

Industries are increasingly looking for opportunities at utilizing collaborative robots in assembly lines to perform the tasks independently or assist the human workers due to the advancement of industry 4.0 technologies. Purchasing cost is one of the important factors to be considered by production managers, while designing or redesigning assembly line when collaborative robots are being utilized. Several objectives are to be optimized in an assembly line balancing problem and optimizing line efficiency along with purchasing cost sometimes results in conflicting situation. This paper presents the first study to tackle the cost-oriented assembly line balancing problem with collaborative robots, where several different types of collaborative robots with different purchasing costs are available and selected. A multi-objective mixed-integer programming model is developed to minimize the cycle time and the total collaborative robot purchasing cost. The multi-objective migrating bird optimization algorithm is developed to obtain a set of high-quality Pareto solutions. This algorithm utilizes the fast non-dominated sorting approach to update the population and develops a restart mechanism to select one solution in the permanent Pareto archive to replace the abandoned solution which remains unchanged for several iterations. The computational study validates that the utilization of the multi-objective model is reasonable and developed algorithm produces competing performance in comparison with multi-objective non-dominated sorting genetic algorithm II, multi-objective simulated annealing algorithm and two multi-objective artificial bee colony algorithms.

An efficient SLM technique based on migrating birds optimization algorithm with cyclic bit flipping mechanism for PAPR reduction in UFMC waveform

The peak-to-average power ratio (PAPR) is one of the most serious issues to be dwelled on, carefully, in wireless communication. It is not possible to take the advantage of multicarrier transmission strategy without handling the problem of high PAPR arising from the usage of multiple subcarriers for generating the time domain transmission signals. As being a kind of multicarrier waveform, universal filtered multicarrier (UFMC) which is developed a short time ago as a contender waveform for the fifth generation (5G) telecommunication systems, suffers from the high PAPR values of its transmission signals as well. By considering the related issue, we developed a new selective mapping (SLM) technique based on migrating birds optimization (MBO) algorithm to offer an efficient solution to the problem of high PAPR in the UFMC waveform. In the proposed MBO-based SLM (MBO–SLM) scheme, in order to perform an effective phase optimization in discrete space, cyclic bit flipping mechanism is integrated to the MBO algorithm. According to the simulation results, with our proposed MBO–SLM technique, significant improvement is achieved in the PAPR reduction performance of the conventional SLM method thanks to the MBO-based phase optimization process supported by the cyclic bit flipping mechanism.

An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

Scalable parallel implementation of migrating birds optimization for the multi-objective task allocation problem

As the distributed computing systems have been widely used in many research and industrial areas, the problem of allocating tasks to available processors in the system efficiently has been an important concern. Since the problem is proven to be NP-hard, heuristic-based optimization techniques have been proposed to solve the task allocation problem. Particularly, the current cloud-based systems have been grown massively requiring multiple features like lower cost, higher reliability, and higher throughput; therefore, the problem has become more challenging and approximate methods have gained more importance. Migrating birds optimization (MBO) algorithm offers successful solutions, especially for quadratic assignment problems. Inspired by the movement of the birds, it exhibits good results by its population-based approach . Since the algorithm needs to deal with many individuals in the population, and the neighbor solution generation phase takes substantial time for large problem instances, we need parallelism to have execution time improvements and make the algorithm practical for large-scale problems. In this work, we propose a scalable parallel implementation of the MBO algorithm, PMBO, for the multi-objective task allocation problem. We redesigned the implementation of the MBO algorithm so that its computationally heavy independent tasks are executed concurrently in separate threads. We compare our implementation with three parallel island-based approaches. The experimental results demonstrate that our implementation exhibits substantial solution quality improvements for difficult problem instances as the computing resources, namely parallelism, increase. Our scalability analysis also presents that higher parallelism levels offer larger solution improvement for the PMBO over the island-based parallel implementations on very hard problem instances.

Transformation Based Tri-Level Feature Selection Approach Using Wavelets and Swarm Computing for Prostate Cancer Classification

Prostate Cancer is a cancer that occurs in the prostate- a small walnut shaped gland in men. This gland helps in the production of seminal fluid which is used to nourish and transport the sperm. One of the most common types of cancer in men is prostate cancer. A microarray dataset contains the microarray gene expression information. On a genome wide scale, gene expression profiles make it easy to analyze the patterns between genes and cancers, however the analysis of gene expression data is very difficult as it has a high dimensionality and low Signal to Noise Ratio (SNR). In this paper, a transformation-based Tri-level feature selection using wavelets for prostate cancer classification has been proposed. For the input microarray data, initially wavelets are applied and then the essential features are selected. Then the standardized gene selection techniques are implemented such as Relief-F, Fishers Score, Information Gain and SNR for a second level feature selection stage. Finally, before proceeding to classification, a third level feature selection by means of optimization techniques are implemented. The optimization techniques incorporated in this work are Marriage in Honey Bee Optimization Algorithm (MHBOA), Migrating Birds Optimization Algorithm (MBOA), Salp Swarm Optimization Algorithm (SSOA) and Whale Optimization Algorithm (WOA). This kind of an approach is totally new, and the best results show when SNR with WOA is classified with Artificial Neural Network (ANN) giving a classification accuracy of 99.48%. The second highest classification accuracy of 99.22% is obtained when Relief-F test with MBOA is classified with Naive Bayesian Classifier (NBC).

A Bottleneck Degree-Based Migrating Birds Optimization Algorithm for the PCB Production Scheduling

To address the printed circuit board shop scheduling problem, a dynamic based on multi-indicator bottleneck degree is proposed to formulate this complex flexible job shop scheduling problem (FJSP). Then, a bottleneck degree-based migrating birds optimization algorithm (MBO) is proposed to tackle this NP-hard problem. Specifically, a multi-indicator assessment model is first developed to obtain the bottleneck degree, and then a two-layer code-decoding structure based on the variable domain structure and the addition of the improved migratory bird algorithm with competitive mechanisms are employed to improve the effectiveness. Finally, the computational experiments demonstrate that the proposed dynamic scheduling approach based on bottleneck degrees and improved migrating bird algorithms can obtain promising results and it outperforms several other algorithms.

An Improved Discrete Migrating Birds Optimization Algorithm for the No-Wait Flow Shop Scheduling Problem

An improved discrete migrating birds optimization (IDMBO) algorithm is presented in this work to solve the no-wait flow shop scheduling problem (NWFSSP) with makespan criterion. In the algorithm, all of the solutions in population are treated as birds aligned in a V formation named the leader and followers. To guarantee the quality and the diversity of initial population, the leader is provided by the standard deviation heuristic (SDH) algorithm, and the rest (the followers) are generated randomly. Given that IDMBO is a neighborhood-based search algorithm, the quality of algorithm depends heavily on the neighborhood structures, where the two variants of the hybrid multi-neighborhood strategy, which are multiply neighborhood structures embedded in variable neighborhood search (VNS) in different forms, are performed to generate the neighborhood solutions for leader and followers, respectively. Furthermore, the population reset mechanism is performed after a given number iterations without improving the current solution. The local search (LS) method can further ameliorate the quality of solutions. The computational study is conducted to analyze the efficiency of IDMBO algorithm on benchmarks designed by Reeve and Taillard. And the comparison results are shown that the presented algorithm is superior to several existing algorithms for NWFFSP.

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.