Metaheuristic optimisation of underground mining ramp designs for cost-efficient excavation and support

In article by Mahmood [1], the results for a genetic algorithm (GA), adaptive genetic algorithm (AGA), and greedy algorithm were not correctly reported in Section 5 due to a programming error[...]

With the rise of big data in cloud computing, many optimization problems have gradually developed into high-dimensional large-scale optimization problems. In order to address the problem of dimensionality in optimization for genetic algorithms, an adaptive dimensionality reduction genetic optimization algorithm (ADRGA) is proposed. An adaptive vector angle factor is introduced in the algorithm. When the angle of an individual’s adjacent dimension is less than the angle factor, the value of the smaller dimension is marked as 0. Then, the angle between each individual dimension is calculated separately, and the number of zeros in the population is updated. When the number of zeros of all individuals in a population exceeds a given constant in a certain dimension, the dimension is considered to have no more information and deleted. Eight high-dimensional test functions are used to verify the proposed adaptive dimensionality reduction genetic optimization algorithm. The experimental results show that the convergence, accuracy, and speed of the proposed algorithm are better than those of the standard genetic algorithm (GA), the hybrid genetic and simulated annealing algorithm (HGSA), and the adaptive genetic algorithm (AGA).

An adaptive genetic algorithm with diversity-guided mutation, which combines adaptive probabilities of crossover and mutation was proposed. By means of homogeneous finite Markov chains, it is proved that adaptive genetic algorithm with diversity-guided mutation and genetic algorithm with diversity-guided mutation converge to the global optimum if they maintain the best solutions, and the convergence of adaptive genetic algorithms with adaptive probabilities of crossover and mutation was studied. The performances of the above algorithms in optimizing several unimodal and multimodal functions were compared. The results show that for multimodal functions the average convergence generation of the adaptive genetic algorithm with diversity-guided mutation is about 900 less than that of adaptive genetic algorithm with adaptive probabilities and genetic algorithm with diversity-guided mutation, and the adaptive genetic algorithm with diversity-guided mutation does not lead to premature convergence. It is also shown that the better balance between overcoming premature convergence and quickening convergence speed can be gotten.

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

This study focuses on a novel variant of the classical two-echelon vehicle routing problem (2E-VRP), termed the two-echelon vehicle routing problem with dual-customer satisfaction (2E-VRP-DS) (i.e. time windows satisfaction and freshness satisfaction) in community group-buying. It is important to obtain better solutions for the 2E-VRP-DS with long-distance distribution in the first echelon and last-mile delivery in the second echelon. Therefore, a new mathematical model is established for the 2E-VRP-DS that incorporates objectives: minimising the total distribution costs, and maximum dual-customer satisfaction (time windows satisfaction, and product freshness satisfaction). To solve the mathematical model, an efficient adaptive genetic hyper-heuristic algorithm (AGA-HH) was proposed, complemented by a k-means clustering approach to generate initial solutions. The adaptive genetic algorithm is considered to be a high-level heuristic, and ten local search operators were considered as low-level heuristics to expand the search region of the solution and achieve robust optimal results. Three sets of experiments were conducted, and the results demonstrated the superiority of AGA-HH in solving the 2E-VRP-DS, showing improvements in distribution costs reduction, time windows compliance, and product freshness preservation. Moreover, sensitivity analyses were carried out to show the influence of the number of DCs and the tolerance range of product freshness, discovering some managerial insights for companies. Future work should consider and investigate VRPs in other new business modes.

Striking hostile ground targets for air force is a crucial way to achieve air superiority in modern warfare. For the maximum efficiency of limited munitions, the optimal weapon target assignment (WTA) scheme should be developed. Aimed at the fact that lots of approaches have been presented for the WTA problem of stationary targets, while less for the ones of moving targets, this paper proposes an adaptive immune genetic algorithm (AIGA) by combining the acceleration and restrain mechanism of immunity system with the adaptive adjustment strategy of crossover operations and mutation operations. Besides, a swap operation is adopted to raise the diversity of individuals and improve the ability of AIGA in search of the goal optimal solution. The simulation result proves the effectiveness and feasibility of the proposed AIGA, and through comparison analysis, advantages of the algorithm over the adaptive genetic algorithm, the immune genetic algorithm and the standard genetic algorithm are revealed.

In recent years cryptographic tokens have gained popularity as they can be used as a form of emerging alternative financing and as a means of building platforms. The token markets innovate quickly through technology and decentralization, and they are constantly changing, and they have a high risk. Negotiation strategies must therefore be suited to these new circumstances. The genetic algorithm offers a very appropriate approach to resolving these complex issues. However, very little is known about genetic algorithm methods in cryptographic tokens. Accordingly, this paper presents a case study of the simulation of Fan Tokens trading by implementing selected best trading rule sets by a genetic algorithm that simulates a negotiation system through the Monte Carlo method. We have applied Adaptive Boosting and Genetic Algorithms, Deep Learning Neural Network-Genetic Algorithms, Adaptive Genetic Algorithms with Fuzzy Logic, and Quantum Genetic Algorithm techniques. The period selected is from December 1, 2021 to August 25, 2022, and we have used data from the Fan Tokens of Paris Saint-Germain, Manchester City, and Barcelona, leaders in the market. Our results conclude that the Hybrid and Quantum Genetic algorithm display a good execution during the training and testing period. Our study has a major impact on the current decentralized markets and future business opportunities.

In the present study, the Genetic Algorithm and the developed Adaptive Genetic Algorithm are used to solve optimization problems faced in modeling of complex systems. While using the Genetic Algorithm and the Adaptive Genetic Algorithm, the most important problem encountered is that these algorithms are prone to getting stuck in local bests. For example, in the complex system data optimization process using the Genetic Algorithm, it has been observed that it is frequently fitted to local bests in a certain period of time. The reasons are that, many mutations cannot be performed in the limited number of iterations, and because the number of individuals is limited, the population is filled with the same set of solutions in a short time. Therefore, the migration operator has been added to the Genetic Algorithm and the Adaptive Genetic algorithm in order to avoid local bests and to provide that these algorithms search in a wider area of the large search space of complex systems. In the present study, we have tested these algorithms using the migration operator in the Lotka Volterra Model. When the results are examined, it is observed that the Adaptive Genetic Algorithm with migration operator outperforms the Genetic Algorithm and the targeted success is achieved in complex system optimization. In the rest of the paper, the algorithms used in the Method Section are explained with outlines. In the Experimental Studies Section, the different algorithms are tested and compared with each other in the Lotka-Voltera model and numerical test functions. In the Conclusions and Discussions Section, brief information is given about general results and future studies.

Due to various interference factors, a pre-planned assembly scheme and its cycle time can be disturbed, resulting in the failure of product delivery on schedule. However, when assembly data of the production line can be obtained in real time, the balance of the assembly line could be dynamically adjusted in case of experiencing serious interferences to optimize its cycle time in time and improve its production efficiency. Therefore, this paper proposes a dynamic rebalancing framework by integrating real time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving a stochastic type-II simple assembly line balancing problem (SSALBP-II). MES (manufacturing execution system) is used to obtain some real time data such as the resource status, operation information and task information and to judge abnormal phenomenon and the overdue delivery caused by interferences. On this basis, the stochastic type-II simple assembly line balance model is constructed, with a new serial two-stage adaptive alternate genetic fireworks algorithm (STAGFA). This new algorithm can incorporate both genetic algorithm and fireworks algorithm to solve the model according to the transformation of population diversity discrimination index. Through the comparison between STAGFA and other algorithms such as fireworks algorithm, genetic algorithm, other improved intelligent algorithms, it is proved that the STAGFA is effective and superior in solving the assembly line (re)balance problem. Then, the rebalanced scheme verified by simulation is dispatched to control the physical assembly line and realize dynamic rebalancing cycle time effectively.

Software testing is an expensive and time-consuming process. These costs can be significantly reduced using automated methods. Recently, many researchers have focused on automating this process using search algorithms. Many different methods have been proposed, all of which using a means of heuristic or meta-heuristic search algorithms. The main problem with these methods is that they are usually stuck in local optima. In this paper, to overcome such a problem, we have combined the firefly algorithm (FA) and asexual reproduction optimization algorithm (ARO). FA is a bio-inspired algorithm that is very efficient at exploitation and local searches; however, it suffers from poor exploration and is prone to local optima problem. On the other hand, ARO can be used for escaping from local optima. For this combination, we have inserted ARO into the steps of FA for increasing the population diversity. We have utilized this combination for automatic test case generation with the aim of covering all finite paths of the control flow graph. To evaluate the performance of the proposed method, we have utilized it for generating test cases for a number of programs. Results have indicated that, while giving similar results in terms of the test coverage, the proposed method is significantly better than the existing state of the art algorithms in terms of the number of fitness evaluations. Compared algorithms are FA, ARO, traditional genetic algorithm (TGA), adaptive genetic algorithm (AGA), adaptive particle swarm optimization (APSO), hybrid genetic tabu search algorithm (HGATS), random search (RS), differential evolution (DE), and hybrid cuckoo search and genetic algorithm (CSGA).

In online classroom teaching, the function of teaching system can play an important role in the effectiveness of classroom teaching. How to use genetic algorithm to optimize online classroom teaching system has become a research hotspot. Based on genetic algorithm, this paper proposes an adaptive genetic algorithm model based on the traditional algorithm. After setting the appropriate mutation probability, the model can improve the convergence speed. Moreover, based on adaptive genetic algorithm, combined with the direct value method and BT neural network theory, this paper constructs the online classroom teaching quality evaluation model and the teaching system test paper data model, and optimizes adaptive mutation genetic algorithm and BP neural network to evaluate the teaching effectiveness. Simulation experiments are carried out based on the algorithm model, and the visual parameter values are obtained. After experimental comparison, the initial value of the mutation rate is set between 0.002 and 0.004. For the network classroom teaching system, this paper introduces the system demand analysis, function module design, and database design in detail. Finally, through the questionnaire survey, this paper understands the network situation of students in class and the use of online classroom teaching platform in detail, analyzes the problems and influencing factors of online teaching, and finally puts forward the strategies to improve the effectiveness of online classroom teaching.

Friction is an inherent nonlinear disturbance that can lead to creeping, jitter, and decreased tracking precision in an electro-hydraulic servo system. In this paper, the LuGre friction model is used to describe the dynamic and static characteristics of the friction force of a servo system comprehensively. Accurate identification of model parameters is key to implementing friction compensation. However, traditional genetic identification algorithms have the shortcomings of a premature solution, slow convergence, and poor accuracy. To address these shortcomings, this paper proposes an improved adaptive genetic identification algorithm. The proposed algorithm selects evolutionary processes adaptively according to the population concentration in the initial stage of population evolution. Moreover, it adjusts the crossover probability and the mutation probability to identify a local optimum accurately and converge to the global optimum rapidly. During the late stage of population evolution, the accuracy of the global optimal solution can be improved by reducing the search range of identification parameters. The simulation results show that the relative error of the model parameter values identified by the proposed algorithm is reduced to less than 1% and the convergence speed is faster. Compared with the existing traditional genetic algorithm and adaptive genetic algorithm, the overall performance of the proposed method is better. This study provides a feasible and highly accurate identification method for parameter identification of friction models used in electro-hydraulic servo systems.

This paper proposes the integration of simulated annealing into an adaptive genetic algorithm in order to solve reactive power optimization problems. The idea of using a genetic algorithm as global search strategy while simulated annealing is used local improvements, helps to improve the optimization if done appropriately. The decimal encoding and decoding are used. The flow chart of proposed algorithm is presented and the parameter of the proposed hybrid algorithm is illustrated. The steps of algorithm procedure are designed. Two systems of IEEE 14-bus and IEEE 30-bus are tested. The results show that the proposed algorithm in the paper is more feasible and effective.

Due to the critical blood shortages in South Africa and around the world, the assignment of blood can be considered an important real world optimization problem. This paper presents a mathematical model that facilitates good management and assignment of red blood cell units in order to minimize the quantity of imported units from outside the system. The model makes use of the Multiple Knapsack Algorithm, which is implemented using several optimization techniques, in order to determine the most optimal assignments. These include a Genetic Algorithm (GA), Adaptive Genetic Algorithm (AGA), Simulated Annealing Genetic Algorithm (SAGA), Adaptive Simulated Annealing Genetic Algorithm (ASAGA) and finally a Hill Climbing (HC) Algorithm. All techniques were capable of achieving the optimal fitnesses. The AGA, SAGA and ASAGA provide some desirable results over the standard GA, whilst the HC algorithm proves to demonstrate the best results overall.

We develop an adaptive and efficient genetic algorithm (GA) to solve the dynamic routing, modulation and spectrum assignments (RMSA) for elastic O-OFDM networks. The algorithm offers an efficient way of serving the dynamic lightpath requests based on the current network status at each service provision time. The GA is designed for multi-objective optimization. For low traffic cases when there is no blocking, the GA minimizes the maximum number of slots required on any fiber in the network; otherwise, it minimizes the blocking probability. The performance of the proposed GA is evaluated in dynamic RMSA simulations with the 14-node NSFNET and the 28-node US Backbone topologies, and the results show that it converges within 25 generations. The simulation results also verify that the GA-RMSA outperforms several existing algorithms by providing more load-balanced network provisioning solutions with lower blocking probabilities. Specifically, when the traffic load is same, the GA can achieve more than one order-of-magnitude reduction on blocking probability. To the best of our knowledge, this is the first attempt to solve dynamic RMSA in elastic O-OFDM networks with a GA.