Elitist Selection Strategy Research Articles

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

In order to achieve the goal of low-carbon, efficient delivery using unmanned vehicles, a multi-objective optimization model considering carbon emissions in the problem of optimizing multi-route delivery for unmanned vehicles is proposed. An improved genetic algorithm (IGA) is designed for solving this problem. This study takes into account constraints such as the maximum service duration for delivery, the number of vehicles, and the approved loading capacity of the vehicles, with the objective of minimizing the startup cost, transportation cost, fuel cost, and environmental cost in terms of the carbon dioxide emissions of unmanned vehicles. A combination encoding method based on the integer of the number of trips, the number of vehicles, and the number of customers is used. The inclusion of a simulated annealing algorithm and an elite selection strategy in the design of the IGA enhances the quality and efficiency of the algorithm. The international dataset Solomon RC 208 is used to verify the effectiveness of the model and the algorithm in small-, medium-, and large-scale cases by comparing them with the genetic algorithm (GA) and simulated annealing algorithm (SA). The research results show that the proposed model is applicable to the problem of optimizing the multi-route delivery of unmanned vehicles while considering carbon emissions. Compared with the GA and SA, the IGA demonstrates faster convergence speed and higher optimization efficiency. Additionally, as the problem’s scale increases, the average total cost deviation rate changes significantly, and better delivery solutions for unmanned vehicles are obtained with the IGA. Furthermore, the selection of delivery routes for unmanned vehicles primarily depends on their startup costs and transportation distance, and the choice of different vehicle types has an impact on delivery duration, total distance, and the average number of trips. The delivery strategy that considers carbon emissions shows a 22.6% difference in its total cost compared to the strategy that does not consider carbon emissions. The model and algorithms proposed in this study provide optimization solutions for achieving low-carbon and efficient delivery using unmanned vehicles, aiming to reduce their environmental impact and costs. They also contribute to the development and application of unmanned vehicle technology in the delivery field.

A modified evolutionary reinforcement learning for multi-agent region protection with fewer defenders

Autonomous region protection is a significant research area in multi-agent systems, aiming to empower defenders in preventing intruders from accessing specific regions. This paper presents a Multi-agent Region Protection Environment (MRPE) featuring fewer defenders, defender damages, and intruder evasion strategies targeting defenders. MRPE poses challenges for traditional protection methods due to its high nonstationarity and limited interception time window. To surmount these hurdles, we modify evolutionary reinforcement learning, giving rise to the corresponding multi-agent region protection method (MRPM). MRPM amalgamates the merits of evolutionary algorithms and deep reinforcement learning, specifically leveraging Differential Evolution (DE) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). DE facilitates diverse sample exploration and overcomes sparse rewards, while MADDPG trains defenders and expedites the DE convergence process. Additionally, an elite selection strategy tailored for multi-agent systems is devised to enhance defender collaboration. The paper also presents ingenious designs for the fitness and reward functions to effectively drive policy optimizations. Finally, extensive numerical simulations are conducted to validate the effectiveness of MRPM.

A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem

In recent years, the flexible job shop dynamic scheduling problem (FJDSP) has received considerable attention; however, FJDSP with transportation resource constraint is seldom investigated. In this study, FJDSP with transportation resource constraint is considered and an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm (DQNSGA) integrated with reinforcement learning (RL) is proposed. In DQNSGA, an initialization method based on heuristic rules and an insertional greedy decoding approach are designed, and a double-Q Learning with an improved ε-greedy strategy is used to adaptively adjust the key parameters of NSGA-III. An improved elite selection strategy is also applied. Through extensive experiments and practical case studies, this algorithm has been compared with three other well-known algorithms. The results demonstrate that DQNSGA exhibits significant effectiveness and superiority in all tests. The research presented in this paper enables effective adjustments of production plans in response to dynamic events, which is of critical importance for production management in the manufacturing industry.

Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm

The conformal antenna is a precision device installed on the wing of an aircraft, and its components are distributed on a curved surface. Quality detection is required after assembly. In solving the path planning problem for conformal antenna surface detection, the traditional genetic algorithm faces problems such as slow convergence and easily falling into a local optimal solution. To solve this problem, an improved genetic algorithm combining the historical optimal population (CHOP-IGA) is proposed. First, the algorithm uses the probability-based four-nearest-neighbor method to construct an initial population. Subsequently, the probabilities of the crossover and mutation operators are dynamically adjusted. Next, the algorithm applies the crossover and mutation operators to the population and performs mutation operations on each individual of the historical optimal population. Then, the fitness value is calculated and the next generation of individuals is selected from the parent, offspring, and historical optimal populations according to the elite selection strategy. Finally, the current best fitness is checked to determine whether updating the historical optimal population is necessary. When the termination condition is satisfied, the algorithm outputs the optimal result. Experiments showed that the algorithm satisfactorily solved the path planning problem for conformal antenna surface detection, with a 48.44% improvement in detection efficiency.

An improved particle swarm optimization with particle refactor operator for perishable food delivery problems by electric vehicles

ABSTRACT The increasing demand for perishable food and the popularity of electric vehicles have promoted the integration research of perishable food delivery services and electric vehicles. Aiming at minimizing the total delivery cost, a new model is formulated for perishable food delivery problems by electric vehicles (PFDP-EV), which considers vehicle capacity constraints, travel time constraints, time window constraints, and so on. An improved particle swarm optimization with particle refactor operator (IPSO-PRO) is developed to solve the proposed model. For the IPSO-PRO, a particle refactor operator is designed to help reconstruct the unqualified particles, and an elite selection strategy and an adaptive weighted strategy are used to improve the performance. Then, extensive efforts are conducted to verify the proposed method. First, the parameters of IPSO-PRO are tuned based on the Taguchi method. Second, small-scale, medium-scale, and large-scale perishable food delivery instances (19 instances) are simulated to evaluate the performance, and the results show that IPSO-PRO achieves the best average gap of 0%. Finally, based on a simulation case, the result and sensitivity analysis are conducted to reveal insightful management insights, which provides decision support for perishable food delivery problems.

An effective multi-objective bald eagle search algorithm for solving engineering design problems

In this paper, a multi-objective bald eagle search algorithm (MOBES) is proposed. The MOBES introduces an archive mechanism to store the non-dominated solutions obtained by the algorithm. When the archive overflows, remove the most crowded solutions by using the roulette method. The MOBES also adds elite selection strategy to guide other individuals to optimize by selecting elite individuals in the population. The efficiency of MOBES is validated on CEC 2020 benchmark functions, and the results demonstrate that the proposed algorithm is more efficient than its competitors in terms of convergence, diversity and distribution of solutions. The MOBES is also applied to two-objective, tri-objective and four-objective engineering design problems in real world. The results show its superiority in handling challenging multi-objective optimization problems with unknown true Pareto optimal solutions and fronts, and it is more competitive than other algorithms.

Cost-Effective Flight Sequencing and Gate Assignment Considering Transfer Time under Pandemic Conditions

An efficient hub-and-spoke network (HSN) can reduce operating costs and passenger delays at hubs through proper sequencing of flights and assignment of gates. Typically, batches of flights are scheduled to arrive within brief time windows. By considering aircraft sizes/loads and locations of the available gates, there is a considerable potential for reducing the total cost. During pandemic conditions, passenger transfer time and aircraft dwell time significantly increased because of outbreak controls (e.g., COVID-19 and its variant). In addition to walking time between connecting flights, there is a significant delay for the airport staff to validate passengers’ proper travel documents. The objective of this study is to minimize the total cost by optimizing the flight sequencing and gate assignment and by considering the realistic transfer delay under pandemic situations. A genetic algorithm with an elite selection strategy is developed to search for the optimal solution, which significantly reduces the total cost by 25% compared with that under existing operations, and the relations between optimized solutions and various model parameters are explored.

Artificial Intelligence Evaluation for Mathematics Teaching in Colleges under the Guidance of Wireless Network

Comprehensively improving the quality of teaching is the highlight of current higher education. Also, it meets the basic needs of China’s current transformation into a country for strong education. Mathematics is one of the most important subjects in education these days, which is widely applied in research, social life, and understanding various science theories and laws. Hence, improving the quality of teaching mathematics in colleges becomes an important task in modern college teaching. In particular, the wireless integration of information has enriched and diversified the teaching methods of higher education. Vigorously promoting the application of wireless network technology in mathematics teaching is not only an effective way to solve the insufficient supply of educational resources but also a bold attempt to innovate the mathematics teaching mode in colleges and universities. Firstly, this work proposes an IACO-BP (improved ant colony optimization-based backpropagation) network to evaluate teaching quality for higher level mathematics in a wireless network environment. It improves the traditional ant colony optimization algorithm from three aspects, namely ant colony pheromone adaptive volatility coefficient, pheromone iterative elite selection strategy, and population iteration, adding a variation factor to construct IACO. Then, we use IACO to optimize initial weight and threshold to solve the issue of BP network’s falling into local optimum and improve network performance. Secondly, this work puts forward a series of countermeasures for the construction of mathematics teaching system in colleges. The correctness and superiority of the proposed strategy are verified by comparing the mathematics teaching quality in colleges before and after using the proposed strategies. Various aspects of mathematics teaching, such as teaching objectives, teaching content, and teaching process, are evaluated before and after using these strategies. These features are improved by a significant margin, up to 2.6%, after applying the proposed strategies.

Nondominated Sorting Differential Evolution Algorithm to Solve the Biobjective Multi-AGV Routing Problem in Hazardous Chemicals Warehouse

For the multiple automated guided vehicle (multi-AGV) routing problems in the warehousing link of logistics, where the optimization objective is to minimize both the number of AGVs used and the maximum pickup time simultaneously, a nondominant sorting differential evolution (NSDE) algorithm is proposed. In the encoding and decoding stages, the pickup point area is divided. AGVs are allocated to each region according to the proposed rule based on avoiding duplicate paths. Meanwhile, the pickup points within the region can be adjusted to optimize the pickup paths and improve the pickup efficiency. The fast nondominated sorting method and elitist selection strategy in the nondominated sorting genetic algorithm II (NSGA-II) are introduced into the differential evolution algorithm, which sorts all the regions to obtain the best Pareto solution set. Lastly, the domination of the proposed NSDE algorithm in Pareto frontier evaluation indicators is verified by some numerical experiments.

Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm

Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode. The distributed flexible job-shop scheduling problem (DFJSP) has become a research hot topic in the field of scheduling because its production is closer to reality. The research of DFJSP is of great significance to the organization and management of actual production process. To solve the heterogeneous DFJSP with minimal completion time, a hybrid chemical reaction optimization (HCRO) algorithm is proposed in this paper. Firstly, a novel encoding-decoding method for flexible manufacturing unit (FMU) is designed. Secondly, half of initial populations are generated by scheduling rule. Combined with the new solution acceptance method of simulated annealing (SA) algorithm, an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm. Finally, the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters. In the experimental part, the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified. Secondly, in the comparison with other existing algorithms, the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples, but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.

A Path Optimization Algorithm for Multiple Unmanned Tractors in Peach Orchard Management

In order to improve the management efficiency of peach orchards, this paper considers the cooperative operation scheme of multiple unmanned tractors. According to the actual situation, this paper constructs the path planning model of multiple unmanned tractors in a standard peach orchard, designs the objective function to optimize the total turning time and total operating time according to the tractor driving parameters, and solves it by improving the differential evolution algorithm. Aiming at the premature convergence problem, the permutation matrix is introduced to represent the driving paths of multiple unmanned tractors. Then, the dynamic parameters are adopted to make the parameters change with the number of iterations, and the elite selection strategy is used to eliminate the redundant feasible solutions. An Adaptive Elite Differential Evolution (AEDE) algorithm suitable for multi-tractor path optimization is proposed. The results show that, compared with the traditional Differential Evolution algorithm (Differential Evolution, DE), the total turning time and total operating time in the rectangular peach orchard optimized by AEDE are reduced by 3.34% and 0.87%, respectively. Compared with the block operation, the total turning time and total operating time of the AEDE-optimized rectangular peach orchard operation path were reduced by 37.37% and 9.47%, respectively. Experiments show that AEDE, which optimizes the operating path of multi-tractors in standard peach orchards, is able to improve the efficiency and reduce the operating time.

A decomposition-based multi-objective immune algorithm for feature selection in learning to rank

Learning-to-rank (L2R) based on feature selection has been proved effectively. However, feature selection problem is more challenging due to two conflicting objectives, namely, maximizing classification performance and minimizing the number of features. Therefore, in this article, a novel decomposition-based multi-objective immune algorithm for feature selection in L2R, called MOIA/D-FSRank, is proposed. The proposed algorithm associates each solution with a scalar subproblem based on Tchebycheff decomposition approach, which makes the optimization process more efficient. In addition, we propose an Elite Selection Strategy (ESS) in initialization phase, which can significantly improve the diversity and convergence of the initial population. Moreover, the proposed algorithm uses two effective operators, a clonal selection operator and a mutation operator, where the clonal selection operator is to generate clone population to better guide the search direction of the evolution and the mutation operator aims to retain excellent features with a higher probability in evolution. In this paper, the pairwise method with O(n2) size is used to train the ranking model, and extensive experiments are conducted on the four public LETOR benchmark data sets. The experimental results demonstrate that the proposed algorithm can obtain significant performances on the ranking accuracy and the number of features.

Multi-objective Task Scheduling Algorithm Based on Harmony Search for Grid Microservice Optimization

In order to solve the multi-objective task scheduling optimization problem of data platform microservice combination, this paper proposes an improved harmony search algorithm MODRHS, which fuses the multi-objective optimization NSGA-II algorithm, and improve its elite selection strategy by a circular elimination method, and strengthens the uniformity of the non-dominated solutions at first layer and improves the algorithm’s Pareto optimization ability. At the same time, it describes the mathematical expression of the Dockers model and service optimization goals of the data platform microservice scheduling. The improved harmony search algorithm is applied to practical problems. The performance test of MODRHS is carried out through standard test functions and real data sets. Compared with other classic multi-objective optimization algorithms, the convergence and diversity of the MODRHS algorithm have been effectively improved.

Research on new edge computing network architecture and task offloading strategy for Internet of Things

How to effectively utilize edge nodes with limited computing resources to ensure quality of service is a key issue for many end users in Internet of Things. To address this problem, we propose a new cloud-edge computing network architecture, which enables the system to meet the requirements of computing resource and response time. The architecture consists of a powerful cloud computing center, multiple mobile edge computing servers and users in Internet of Things. We jointly optimize the task offloading and resource allocation of end users, thereby constructing a mixed integer nonlinear programming problem in the proposed architecture. To further solve this problem, a joint optimization strategy based on binary custom fireworks algorithm is proposed. This algorithm improves the Gaussian mutation operation in traditional fireworks algorithm by introducing Gaussian mutation probability and elite selection strategy, which makes the mutation directional. Finally, simulation results verify the effectiveness of our proposed joint optimization strategy. Compared with several other newer offloading strategies, the proposed joint optimization strategy can obtain significant performance gains.

Improved multi-objective cuckoo search algorithm with novel search strategies for point-to-point part feeding scheduling problems of automotive assembly lines

PurposeThis paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.Design/methodology/approachFirst, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.FindingsNumerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.Research limitations/implicationsThis study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.Originality/valueBoth limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

Decomposition and Dominance Relation Based Many-objective Evolutionary Algorithm

In recent year, the Many-objective Optimization Problems (MaOPs) have become an increasingly hot research area in evolutionary computation. However, it is still a difficult problem to achieve a good balance between convergence and diversity on solving various kinds of MaOPs. To alleviate this issue mentioned above, a Decomposition and dominance relation based many-objective Evolutionary Algorithm(DdrEA) is proposed in this paper. Firstly, the population is decomposed into numbers of sub-populations by using a set of uniform weight vectors, in which they are optimized in a cooperative manner. Then, the fitness value of solution in each sub-population is calculated by angle dominance relation and angle. Finally, elite selection strategy is performed according to its corresponding fitness value. That is, in each subspace, the solution with the smallest fitness value is selected as the elite solution to enter the next generation. Comparing with several high-dimensional and multi-objective evolutionary algorithms (NSGA-II/AD, RVEA, MOMBI-II), the experimental results show that the performance of the proposed algorithm DdrEA is better than that of the comparison algorithm, and the convergence and diversity of the population can be effectively balanced.

A multi-objective immune algorithm for intrusion feature selection

Feature selection plays a crucial role in classification problems, which tries to remove redundant or irrelevant features by mapping high-dimensional data to low-dimensional ones. Thus, this approach can improve the classification accuracy and reduce the computational cost to train the classification model. In this paper, we suggest an improved multi-objective immune algorithm (MOIA) for feature selection in intrusion detection. Specifically, the feature subsets for intrusion detection are treated as the individuals for immune optimization, which will select suitable feature subsets in order to reduce the dimensions of the dataset. After that, a neural network is used to train the classification model using the selected suitable feature subsets, and the output of the classification model is regarded as the target fitness value for each individual. As multiple attack types are considered in this paper, a traditional MOIA is modified by using an elite selection strategy based on the reference vectors, which can maintain the individuals with more promising performance when distinguishing more than five attack types in intrusion detection. By this way, the proposed algorithm can accelerate the convergence speed of classification, which also improves the classification accuracy. Experimental results on the NSL-KDD and UNSW-NB15 datasets validate the higher classification accuracy of the proposed algorithm.

Optimization algorithm of fireworks explosion based on elevator

The fireworks algorithm proposed in recent years. Compared with other traditional optimization algorithms, the fireworks algorithm has a strong ability to solve optimization problem. This paper improves the fireworks algorithm and proposes an elitism based fireworks explosion optimization algorithm (E-FWA). The E-FWA algorithm improves the explosion radius of fireworks algorithm and adds elite selection strategy to fireworks algorithm. A large number of experiments show that the elitism based fireworks explosion optimization algorithm can reduce the search time, improve the convergence of the algorithm, and avoid falling into the local optimal value.

Privacy protection based on many‐objective optimization algorithm

SummaryIt is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many‐objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many‐objective optimization problems (MaOPs) and compared with five state‐of‐the‐art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.

A hybrid model integrating improved flower pollination algorithm-based feature selection and improved random forest for NOX emission estimation of coal-fired power plants

Combustion optimization is an effective technique for energy conservation in power plants. An accurate NOX emission estimation model is crucial for combustion optimization. A novel NOX emission estimation model is proposed that integrates an improved random forest (RF) and a wrapper feature selection based on an improved binary flower pollination algorithm (FPA). Three improvements to the basic FPA are proposed, an elite-selection strategy, a mutation operator, and a dynamic switch probability. Moreover, the optimization precision of the proposed FPA is compared to those of five other optimization algorithms. A feature selection method based on the proposed FPA is employed to identify the optimal inputs for the NOX emission model. In addition, a fine-tuning mechanism to update the population (based on mutual information) is designed and embedded in the feature selection process to speed up convergence. To improve the basic RF, an ensemble pruning algorithm based on a back propagation neural network that can evaluate the importance of each tree and prune “detrimental” trees is developed. With the optimal inputs, the NOX emission model is constructed using the improved RF. The prediction results demonstrate that it is of higher accuracy and better robustness compared to the basic RF and the three other models. The designed feature selection could also decrease the computational complexity and improve the prediction accuracy.