Migrating Birds Optimization Research Articles

An advanced symbol detection approach for MIMO-FBMC/OQAM scheme based on migrating birds optimization algorithm

Application of multiple-input multiple-output (MIMO) technology to the filter bank multicarrier/offset quadrate amplitude modulation (FBMC/OQAM) system provides great improvements on its robustness to channel fading effects. Nevertheless, the unique qualities of MIMO-FBMC/OQAM scheme do not make any sense without solving the symbol detection problem on its receiver part. To this end, an efficient symbol detecting strategy having the capability of recovering the transmitted symbol sequences with the highest accuracy is needed for the related system. Maximum likelihood (ML) detector is known with its excellent symbol detection performance in the literature. However, due to the usage of exhaustive search procedure, the computational complexity of the ML detector reaches extremely high levels with the increase of antenna size and modulation order. On the other hand, in the case that the exhaustive search procedure is substituted with the symbol optimization process, it becomes possible to achieve a large amount of complexity reduction in the conventional ML scheme without compromising too much on its symbol detection performance. In order to carry out an efficient symbol optimization in discrete space, we propose migrating birds optimization (MBO) algorithm based on cyclic bit flipping procedure in this paper. By virtue of employing the proposed MBO algorithm reinforced by the cyclic bit flipping mechanism for optimizing the symbol sequences, the computational complexity of the conventional ML is reduced to quite low levels. The percentages of complexity reduction achieved by the proposed scheme over the classical ML for 4 × 4, 6 × 6 and 8 × 8 MIMO configurations are equal to 29.688%, 87.188% and 98.299%, respectively. In addition to these huge complexity gains, an efficient symbol detection performance quite near to that of conventional ML is obtained thanks to the usage of aforementioned MBO-based symbol optimization procedure.

Migrating birds optimization-based feature selection for text classification

Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Migrating Birds Optimization (MBO) approach with naïve Bayes as an internal classifier to address these challenges. The motivation behind this study stems from the recognized limitations of existing techniques in efficiently handling extensive feature sets. Traditional approaches often fail to adequately streamline the feature selection process, resulting in suboptimal classification accuracy and increased computational overhead. In response to this need, our primary objective is to propose a scalable and effective solution that enhances both computational efficiency and classification accuracy in text classification systems. To achieve this objective, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62,221 to 2,089. Through extensive experiments, we demonstrate the superior effectiveness of MBO-NB in feature reduction compared to other existing techniques, resulting in significantly improved classification accuracy. Furthermore, the successful integration of naïve Bayes within MBO offers a comprehensive and well-rounded solution to the feature selection problem. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research provides valuable insights into enhancing feature selection methods, thereby contributing to the advancement of text classification techniques. By offering a scalable and effective solution, MBO-NB addresses the pressing need for improved feature selection methods in text classification, thereby facilitating the development of more robust and efficient classification systems.

Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental Machine Learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.

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.

Turkish Cashier Problem with time windows and its solution by matheuristic algorithms

Turkish Cashier Problem (TCP) is a new application area of the traveling salesman problem that was introduced to the literature recently. In this problem, the cashier can use public transportation or take a taxi where the cashier must visit multiple customer locations while minimizing the total transportation cost. In this study, we introduce a more realistic version of this problem where time has been integrated. This aspect is achieved by imposing time intervals within which the cashier must visit the customers. We name this problem as the TCP with time windows (TCPwTW). We develop several matheuristic algorithms to solve the TCPwTW: a modified version of the Simplify and Conquer (SAC) algorithm that was suggested for the TCP, simulated annealing (SA), original and modified versions of the migrating birds optimization (MBO) algorithm coupled with mathematical programming. We also tried to find the exact optimum using a Solver where for complex problems, only lower bounds were found. Numerical experimentation reveals that while for problems with loose time intervals, an exact solver can be considered. Once the time intervals tighten up, the best solutions can be obtained using matheuristics involving SA and MBO.

Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation

For the hydraulic cylinder parts manufacturing shop scheduling problem (HCPMS), which integrates a parallel batch processor hybrid flow shop scheduling problem with the flexible job shop scheduling problem, this paper establishes a multi-objective scheduling model with makespan, total energy consumption, and total machine workload as the optimisation objectives, and proposes an improved multi-objective migrating birds optimisation (IMOMBO) algorithm to solve the problem. First, considering the characteristics of the combination of single-piece and batch processing in the workshop, a double-layer coding rule based on the operation and processing equipment is proposed, and the corresponding decoding rule is designed according to whether the workpiece requires quenching and tempering. Second, a multi-population co-evolution mechanism is developed to enhance the diversity of solutions by conducting different evolutionary strategies. Additionally, six neighborhood structures are introduced to perform local searches for the leader and follower birds, thereby improving the quality of the solutions. Finally, the effectiveness of the IMOMBO algorithm is demonstrated by comparing its results with those of four other algorithms through comparative experiments and a practical case.

Multi-objective migrating birds optimization for solving stochastic home health care routing and scheduling problems considering caregiver working time constraints

Currently, many countries are suffering from a heavy burden caused by population aging since the elderly occupy a large number of medical resources. Home health care (HHC) is a prospective approach to relieve and cope with this issue. This study addresses a multi-objective HHC routing and scheduling problem (HHCRSP) considering uncertainties and caregivers’ working time balance. First, a stochastic programming method is employed to formulate the uncertain service time and travel time, and then a chance-constrained programming model is developed to minimize the total operation cost and penalty cost of violating allowable time windows. Second, a multi-objective migrating birds optimization incorporating a stochastic simulation method is proposed. An ensemble of population iteration and external archive search is designed to obtain high-quality solutions. A stochastic simulation strategy is devised to evaluate the feasibility of obtained solutions and calculate their objective values. At last, numerical experiments are implemented for solving an extending benchmark set. The comparisons and discussions verify the better performance of the proposed algorithm compared with its peers.

Energy Efficiency in Green Industry: A Meta-heuristic Approach

This paper addresses the challenge of optimizing energy efficiency in production shop scheduling problem, focusing specifically on the constraint of sequence-dependent setup times (FSSP SDST). This study is conducted in the context of promoting green industrial practices. The main objective is to minimize total energy consumption (TEC) in the manufacturing process. We introduce a mathematical model of this problem. To solve this problem, we use the Artificial Bee Colony algorithm (ABC). In addition, the Migrating Birds Optimization (MBO) algorithm will be presented. The effectiveness of the proposed algorithms will be subjected to rigorous evaluation through detailed tests on different instances. This methodical approach will enable us to carry out an in-depth and detailed comparative analysis, highlighting the relative strengths and weaknesses of the algorithms presented in this study.

Designing adaptive smart buildings: an RNN and Migrating Birds Optimization approach for occupancy prediction

Designing adaptive smart buildings: an RNN and Migrating Birds Optimization approach for occupancy prediction

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.

Bi-Objective Modeling and Optimization for Stochastic Two-Stage Open Shop Scheduling Problems in the Sharing Economy

Nowadays, many manufacturing and service industries prefer to share resources such as facilities and workers to cooperatively perform tasks, which can efficiently improve resource utilization and customer satisfaction. Generally, the decision-makers need to pay more for resource usage, leading to an urgent demand to decrease operational costs. This article proposes a stochastic bi-objective two-stage open shop scheduling problem that models a vehicle maintenance process where tasks are appointed to be completed by multiple third-party companies with professional equipment. We formulate this optimization problem by minimizing the total tardiness and processing cost subject to various resource constraints. A hybrid multiobjective migrating birds optimization combined with a genetic operation and a discrete event system is designed by considering problem characteristics to solve the problem. In this method, the migrating birds optimization with some particular strategies aims at searching candidate solutions from the entire solution domain. Simultaneously, the discrete event system, by using stochastic simulation and discrete event-based simulation approaches, focuses on evaluating the performance of searched solutions. Simulation experiments are performed, and state-of-the-art algorithms are used as competitive approaches. The results confirm that this approach has an excellent performance in handling our considered problem.

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

Recent Applications and Advances of Migrating Birds Optimization

Recent Applications and Advances of Migrating Birds Optimization

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.

Natural Inspired Learning Path Recommendation System for Students in E-learning Platforms

Abstract: E-learning has emerged as one of the advanced and efficient methods of education, especially during the ongoing pandemic. While many learning platforms have integrated recommender systems to aid learners in understanding and improving their skills, it has been observed that this approach alone may not be sufficient for optimal learning outcomes. Therefore, this paper proposes a unique method and algorithm inspired by bird swarms to recommend an efficient learning path for learners. The effectiveness of the proposed model was evaluated using nearly three hundred and ninety-five student records, and qualitative evaluation metrics were used to assess its performance. Keywords: E-learning, Nature-inspired algorithms, Recommendation systems, Migrating birds optimization.

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

Energy Management in Mobile Ad Hoc Networks with Innovative Protocol TDQR CNFPQR and Bird Optimization Technique

It is impossible to stress the importance of a Dynamic Wireless Ad Hoc Network (MANET) for message path dependability and permanence. Data sharing is a vital activity in a mobile ad hoc network because packet loss occurs when nodes fail or are missing during data transfer. When a packet is lost, it can be subject to various infractions. To overcome this problem, a new algorithm was created known as TDQR CNFPQR (Trigger-Based Distributed QoS Clustered Node Failure Prediction QoS) Protocol. The MBO method analyses node state to minimize packet losses. In comparison to existing methodologies, our suggested methodology uses less energy, delivers more packets, and has a lower routing burden and higher end-to-end latency. Mobile station users can receive stationary network services offered via numerous jump links even if the network is not immediately available to them. Because wireless networks have limited route capacity, it is critical to react to user requests as rapidly as feasible. Because network nodes have limited energy resources, it is critical to spend as little energy as possible when transferring data across the network. Ad hoc wireless networks are hampered by limited battery power, making energy management a critical concern. Knowledge-based algorithm rule analyses the node's monitoring capabilities at the same time. We can then predict node failure, node longevity, and data exchange along the ideal path without packet loss using Migrating Birds Optimization (MBO).

Energy, economic, and environmental (3 E) examinations of the indirect-expansion solar heat pump water heater system: A simulation-oriented performance optimization and multi-objective decision-making

In this paper, a simulation-oriented optimization integrated with the multi-objective decision-making (MODM) model for an indirect-expansion solar-assisted heat pump (IDX-SAHP) water heater is proposed and the energy, economic, and environmental (3 E) aspects of the system are examined. The developed model is verified and its performance is scrutinized in a humid continental climate region of China. Next, the sensitivity analysis is conducted via the one-way analysis of variance (ANOVA) approach. Then, the single-objective optimization (SOO) and multi-objective optimization (MOO) are conducted via the migrating birds optimization (MBO) method to maximize the coefficient of performance (COP) and the collector efficiency (CE), singly and concomitantly. The SOO results indicate the IDX-SAHP design as per the COP-oriented optimization causes the system to operate less for about 120 hours a year in contrast to the system design as per the CE-oriented optimization. Also, the results of MOO integrated with MODM indicate that MOO gives better performance in contrast to the SOOs so that although the CE of the maximized IDX-SAHP reduces a little from 51% to 46%, its COP increases greatly from 2.5 to 7. Therefore, the overall power consumption of the system decreases about 269 hours in contrast to the initial IDX-SAHP. Besides, the environmental examination illustrates the use of IDX-SAHP in Beijing buildings can prevent the burning of 2,877, 000, 000 cubic meters of natural gas per year in the fossil fuel power plants and prevent the release of 8,055,600 tons of CO2 per year into the environment. Furthermore, the economical examination illustrates that Beijing residents may save approximately one billion USD per year on the purchase of electricity if they use the IDX-SAHP. Besides, the Chinese government can achieve about 109 billion USD per year if the equivalent electricity is exported instead of domestic consumption for Electric Water Heaters (EWHs). Moreover, the external costs decrease by 928 million USD per year if the IDX-SAHP is replaced with the EWHs. This paper discloses the significance of energy, economic, and environmental (3 E) examinations, optimization, and MODM during the IDX-SAHP design to increase the system efficiency and decrease the costs and environmental impacts by opting the most appropriate design parameters of the system.

A decomposition-based multi-objective evolutionary algorithm for hybrid flowshop rescheduling problem with consistent sublots

ABSTRACT Lot streaming is the most widely used technique to facilitate the overlap of successive operations. Considering the consistent sublots and machine breakdown, this study investigates the multi-objective hybrid flowshop rescheduling problem with consistent sublots (MOHFRP_CS), which aims at optimising the total completion time, starting time deviations of operations, and average adjustment of sublot sizes simultaneously. By introducing the decomposition strategy and effective migrating birds optimisation framework, this paper develops a multi-objective migrating birds optimisation algorithm based on decomposition (MMBO/D). In MMBO/D, the problem is decomposed into a series of sub-problems, and its solutions are initialised by the Glover operator and further optimised by the variable neighbourhood descent strategy. The weights assigned to the sub-problems are adapted dynamically according to a variable weight strategy, and a global update strategy is employed to update the solutions. A novel sharing and benefiting mechanism is proposed to implement coevolution among different sub-problems. Competitive mechanisms are modified by considering similar sub-problems to improve population quality. A criterion is designed to check whether a subproblem is stuck in the local optima. The comprehensive computational results demonstrate that MMBO/D outperforms other state-of-the-art multi-objective evolutionary algorithms (MOEAs) for the addressed problem.