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This paper proposes a hybrid biogeography‐based optimization (BBO) with simplex method (SM) algorithm (HSMBBO).

BBO is a new intelligent optimization algorithm. The global optimization ability of BBO is better than that of genetic algorithm (GA) and particle swarm optimization (PSO), but BBO also easily falls into local minimum. To improve BBO, HSMBBO combines BBO and SM, which makes full use of the high local search ability of SM. In HSMBBO, BBO is used firstly to obtain the current global solution. Then SM is searched to acquire the optimum solution based on that global solution. Due to the searching of SM, the search range is expanded and the speed of convergence is faster. Meanwhile, HSMBBO is applied to motion estimation of video coding.

In total, six benchmark functions with multimodal and high dimension are tested. Simulation results show that HSMBBO outperforms GA, PSO and BBO in converging speed and global search ability. Meanwhile, the application results show that HSMBBO performs better than GA, PSO and BBO in terms of both searching precision and time‐consumption.

The proposed algorithm improves the BBO algorithm and provides a new approach for motion estimation of video coding.

The purpose of this paper is to propose an algorithm that combines the particle swarm optimization (PSO) with the biogeography-based optimization (BBO) algorithm.

The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both algorithms. The efficiency of the proposed algorithm is tested using some selected standard benchmark functions. The performance of the proposed algorithm is compared with that of the differential evolutionary (DE), genetic algorithm (GA), PSO, BBO, blended BBO and hybrid BBO-DE algorithms.

Experimental results indicate that the proposed algorithm outperforms the BBO, PSO, DE, GA, and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE algorithm. However, the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE algorithm.

The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem.

Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used.

Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results.

In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations.

The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications.

By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations.

The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.

The purpose of this paper is to propose a novel Unmanned Combat Air Vehicle (UCAV) flight controller parameters identification method, which is based on predator-prey Biogeography-Based Optimization (PPBBO) algorithm, with the objective of optimizing the whole UCAV system design process.

The hybrid model of predator-prey theory and biogeography-based optimization (BBO) algorithm is established for parameters identification of UCAV. This proposed method identifies controller parameters and reduces the computational complexity.

The basic BBO is improved by modifying the search strategy and adding some limits, so that it can be better applied to the parameters identification problem. Comparative experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee finding the optimal controller parameters, with the rapid convergence.

The proposed PPBBO algorithm can be easily applied to practice and can help the design of the UCAV flight control system, which will considerably increase the autonomy of the UCAV.

A hybrid model of predator-prey theory and BBO algorithm is proposed for parameters identification of UCAV, and a PPBBO-based software platform for UCAV controller design is also developed.

For prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.

This paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.

This paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.

With respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.

The proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.

The value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.

The purpose of this paper is to examine and compare the entire impact of various execution skills of oppositional biogeography-based optimization using the current optimum (COOBBO) algorithm.

The improvement measures tested in this paper include different initialization approaches, crossover approaches, local optimization approaches, and greedy approaches. Eight well-known traveling salesman problems (TSP) are employed for performance verification. Four comparison criteria are recoded and compared to analyze the contribution of each modified method.

Experiment results illustrate that the combination model of “25 nearest-neighbor algorithm initialization+inver-over crossover+2-opt+all greedy” may be the best choice of all when considering both the overall algorithm performance and computation overhead.

When solving TSP with varying scales, these modified methods can enhance the performance and efficiency of COOBBO algorithm in different degrees. And an appropriate combination model may make the fullest possible contribution.

Biogeography‐based optimization algorithm is a new kind of optimization algorithm based on biogeography. It is designed based on the migration strategy of animals to solve the problem of optimization. The purpose of this paper is to present a new algorithm – biogeography migration algorithm for traveling salesman problem (TSPBMA). A new special migration operator is designed for producing new solutions.

The paper gives the definition of TSP and models of TSPBMA; introduces the algorithm of TSPBMA in detail and gives the proof of convergence in theory; provides simulation results of TSPBMA compared with other optimization algorithms for TSP and presents some concluding remarks and suggestions for further work.

The TSPBMA is tested on some classical TSP problems. The comparison results with the other nature‐inspired optimization algorithms show that TSPBMA is useful for TSP combination optimization. Especially, the designed migration operator is very effective for TSP solving. Although the proposed TSPBMA is not better than ant colony algorithm in the respect of convergence speed and accuracy, it provides a new way for this kind of problem.

The migration operator is a new strategy for solving TSPs. It has never been used by any other evolutionary algorithm or swarm intelligence before TSPBMA.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.

An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.

Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.

Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.