Collaborative Optimization Algorithm Research Articles

Collaborative Optimization Algorithm Based on Chameleon Swarm Algorithm Designed for the Problem of Beer Production Scheduling

Aiming at finding a better way to solve the problem of beer production scheduling, a new collaborative optimization based on the Manhattan Distance and Chameleon Swarm Algorithm is proposed. Firstly, a dynamic relaxation factor is introduced to the constraints at the system level, which combines the changing trend of the inconsistency information and the optimal solution of the discipline level. Additionally, the Manhattan Distance is used to replace the square of the Euclidean Distance at the system level. Thirdly, the Chameleon Swarm Algorithm is used to improve the update rule during the process of iteration. As these improvements are applied to the collaborative optimization, the steps of this new algorithm are given. Through the test case of a multivariate function, it can be found that the algorithm has been improved compared to the original algorithm. Then, a model for beer production scheduling is proposed, and the results of the optimization show that the improved collaborative optimization has better optima effectiveness and fewer iterations and is not sensitive to initial points, which proves that the improved collaborative optimization has a better ability to solve the problem of beer production scheduling than normal collaborative optimization and collaborative optimization with fixed relaxation factors.

A double-Q network collaborative multi-objective optimization algorithm for precast scheduling with curing constraints

Precast scheduling is a special case of optimization problem, in which the curing stage requires parallel processing of a batch of jobs. In this study, distributed flexible job shop scheduling with crane transportation, fuzzy processing, and curing constraints is modeled for precast scheduling. Three objectives are considered simultaneously, namely, fuzzy makespan, energy consumption, and economic cost. To solve this complex problem, a bi-level collaborative multi-objective optimization evolution algorithm (BCMOEA) is developed. First, a double-Q network is designed considering different groups of features to generate valuable solutions for the second-level components. Then, a dynamic-adjusted reference point set is embedded to divide the current population into three sub-populations. Furthermore, a collaboration mechanism is developed to learn different knowledge from these sub-populations to balance the convergence and diversity abilities. Moreover, a knowledge driven curing batching heuristic is designed to improve the fuzzy makespan. Finally, a set of instances generated based on the realistic precast process is tested, and detailed comparisons with the state-of-the-art algorithms show the competitive performances of the proposed algorithm.

An efficient Quasi-Affine Transformation Evolutionary algorithm with fixed dimension updating and its application in UAV 3D path planning

Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a kind of swarm-based collaborative optimization algorithm that solves the problem of a position deviation in a DE search by using the co-evolution matrix M instead of the cross-control parameter CR in the differential evolution algorithm (DE). However, QUATRE shares some of the same weaknesses as DE, such as premature convergence and search stagnation. Inspired by the artificial bee colony algorithm (ABC), we propose a new QUATRE algorithm to improve these problems that ranks all the individuals and evolves only the poorer half of the population. In an evolving population, individuals of different levels intersect with dimensions of different sizes to improve search efficiency and accuracy. In addition, we establish a better selection framework for the parent generation individuals and select more excellent parent individuals to complete the evolution for the individuals trapped in search stagnation. To verify the performance of the new QUATRE algorithm, we divide the comparison algorithm into three groups, including ABC variant group, DE variant group, and QUATRE variant group, and the CEC2014 test suite is used for the comparison. The experimental results show the new QUATRE algorithm performance is competitive. We also successfully apply the new QUATRE algorithm on the 3D path planning of UAV, and compared with the other famous algorithm performance it is still outstanding, which verifies the algorithm’s practicability.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Collaborative optimization model and algorithm for airport capacity and traffic flow allocation.

How to efficiently utilize the existing airport capacity without physical expansion and considerable economic inputs to meet air traffic needs is one of the important tasks of air traffic management. To improve the efficiency of capacity utilization, it is necessary to find the actual airport capacity properly. In this work, taking Shuangliu International Airport as an example, a methodology for capacity estimation is proposed that combines the empirical method with an analytical approach that uses historical performance data from the airport to construct a capacity envelope to approximate the airport's actual capacity to the greatest extent, establishes a collaborative optimization model that reflects the inherent relations between airport capacity and arrival and departure traffic demand, adopts an improved optimization algorithm to solve the model, and generates an optimal flight allocation scheme. Priority ratio is introduced to dynamically adjust management preferences for arrival and departure traffic demand to further reveal the synergy mechanism between departure and arrival traffic flow demand and the airport capacity. The result shows that the Flight On-time Performance rate is lifted by 6% in the case study which proves the feasibility of the proposed method, demonstrating its value for maximizing airport capacity and traffic flow demand without requiring expansions on airport scales.

Collaborative optimization algorithm for electric vehicle industry chain based on regional economic development needs

Abstract With the development of the economy, many regions have experienced a slowdown in economic growth. In order to promote the development of the electric vehicle (EV) industry, the country has also begun to introduce various policies to encourage the development of the EV industry. In this context, many local governments have begun to introduce policies and measures related to the development of the EV industry, such as increasing land use for the development of the EV industry and increasing support for the new energy automobile industry. These policy measures have played a positive role in promoting the development of the EV industry, but there are also some problems. For example, when many local governments introduce policies to support the development of the new energy automobile industry, their support for the EV industry is not significant. This article studied the collaborative optimization of the EV industry chain in response to issues such as insufficient technical strength, imbalanced supply-demand relationship, and insufficient downstream service chain capabilities in the EV industry chain. This article analyzed the composition of the EV industry chain and established an EV industry chain model to address these issues. This article used collaborative optimization algorithms to analyze the production volume of EVs in the EV industry chain, as well as the comprehensive efficiency, pure technical efficiency, and scale efficiency values of upstream, midstream, and downstream. Through experimental analysis, it was found that the comprehensive efficiency value of the upstream of the EV industry chain after using the collaborative optimization algorithm was 0.0792 higher than before. The research results of this article have provided reference significance for the analysis of collaborative optimization algorithms in other fields.

A Cooperative Silicon Content Dynamic Prediction Method With Variable Time Delay Estimation in the Blast Furnace Ironmaking Process

Online assessment of the molten iron quality of a blast furnace ironmaking process strongly depends on reliable measurement of silicon content (Si). This article proposes a novel cooperative strategy to train the data-driven prediction model and estimate variable time delay (VTD) values. First, a dynamic deep network that stacks denoising autoencoders (D-SDAE) with the ability to describe process nonlinearity and time-varying is designed for Si estimation. Then, time delay between the process variables and Si, which may disrupt the data distribution pattern (i.e., the real input-output relationship), is recovered in the modeling process. It is an overlooked data feature that occurs due to material transportation times, installation location and the analysis period of sampling equipment. The VTD values are considered to be the parameters of the D-SDAE model and obtained in the training process by the proposed double-scale collaborative search particle swarm optimization algorithm. The effectiveness of the proposed VTD-based D-SDAE model is validated in a numerical simulation and an industrial ironmaking plant, and a marked improvement in the prediction performance is achieved when VTD information is considered.

Multiobjective Integrated Optimal Control for Nonlinear Systems.

The multiobjective optimal control method optimizes the performance indexes of nonlinear systems to obtain setpoints, and designs a controller to track the setpoints. However, the stepwise optimal control method that independently analyzes the optimization process may obtain unfeasible and difficult to track setpoints, which will reduce the operation and control performance of the systems. To solve this problem, a multiobjective integrated optimal control (MIOC) strategy is proposed for nonlinear systems in this article. The main contributions of MIOC are threefold. First, in the framework of multiobjective model predictive control, an integrated control structure with a comprehensive cost function and a collaborative optimization algorithm is designed to achieve the coordinate optimal control. Second, for the time inconformity of setpoints and control laws caused by the characteristic of tracking control, the different prediction horizons are designed for the comprehensive cost function. Then, the collaborative optimization algorithm is proposed for the comprehensive cost function to achieve the integrated solution of setpoints and control laws to enhance the operation and control performance of nonlinear systems. Third, the stability and control performance analysis of MIOC is provided. Finally, the proposed MIOC method is applied for a nonlinear system to demonstrate its effectiveness.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

Distributed Data Real-Time Transaction Calculation Based on Collaborative Optimization and Multi-Objective Genetic Algorithm

Based on the above situation, this article elaborated on the methods that should be used to calculate the multi-information genetic algorithm (GA) in the current situation. The article mainly compared the non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) with elite strategy. By measuring the solving speed and quality of the two algorithms, it was found that the NSGA-II had a greater advantage. Based on the NSGA-II, optimization processing was carried out. The NSGA-II was compared before and after optimization. After analyzing 48 data samples, it was found that the results of the NSGA-II before and after optimization showed that the algorithm tended to be more stable after optimization, thus indicating that the improved data was more accurate. The results indicated that the NSGA-II was necessary for its improvement, and its results were also reasonable.

A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction

To address the limitation of existing excavator optimization methods, which primarily focus on the force performance while neglecting energy consumption and fail to realize environmentally friendly and low-carbon designs, this paper proposes a new multi-objective collaborative optimization method for an excavator to reduce energy consumption during the working process while maintaining optimal performance. By formulating a mathematical model that quantifies the energy consumption during the working process, this paper optimizes the working conditions by analyzing the energy consumption characteristics under typical working conditions. To overcome the limitation of existing linear weighting methods in multi-objective optimization, such as imbalanced optimization quality among sub-objectives, this paper proposes a new modeling approach based on the loss degree of sub-objectives. A multi-objective collaborative optimization model for the excavator with reduced energy consumption is established, and a corresponding multi-objective collaborative optimization algorithm is developed and applied to achieve optimal solutions for sub-objectives. The optimization results demonstrate that applying the new multi-objective collaborative optimization method to the excavator achieves better optimization quality than traditional methods. It also provides a more balanced improvement in the optimization values of each sub-objective, resulting in a significant reduction in the energy consumption of the excavator during operation.

Multi-topology hierarchical collaborative hybrid particle swarm optimization algorithm for WSN

Multi-topology hierarchical collaborative hybrid particle swarm optimization algorithm for WSN

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

Validation experiment design of degradation models for composite materials with interval uncertainties

The degradation model for composite materials with interval uncertainties is a mathematical representation of the residual properties in the degradation process. And validation experiments are necessary to validate the match between the prediction model and the actual degradation process. Generally, an economic validation experiment plan is essential for engineering practice. As the key to model validation, a novel model metric is established based on the overlap of the interval processes, which can accurately characterize the agreement between the prediction model and the experimental data. Since the new metric utilizes an unbiased estimation method to quantify the interval bounds of experimental data, it can reduce the need for experimental sample size. Furthermore, a validation experiment design optimization model is developed using the prior information obtained from the predictive model, where the number and distribution of observation instants, and the number of experimental samples are the design variables. To solve the optimization problem effectively, a collaborative optimization algorithm is proposed to obtain the optimal distribution of observation instants and sample size. Finally, an existing degradation model of composite materials is utilized to verify the feasibility and effectiveness of the presented experimental design method in engineering applications.

5G MEC-Based Intelligent Computation Offloading in Power Robotic Inspection

Power robotic inspection plays a critical role in the realization of real-time visualization and perception of substation in power grid. 5G mobile edge computing (MEC) has emerged as a promising solution to provide the large bandwidth, wide connectivity, and proximate computing capabilities for the computation offloading of power robotic inspection with stringent delay requirements. This article proposes a 5G MEC-based intelligent computation offloading framework in power robotic inspection to cope with multi-dimension entity heterogeneity, environment dynamics, and inspection delay guarantee. Specifically, the proposed framework and the implementation procedures of computation offloading are firstly elaborated, and the research challenges are outlined. Then, we propose an artificial intelligence (AI)-enabled multi-dimension collaborative optimization algorithm of route planning and task offloading to address the low-latency computation offloading problem under queue stability constraint. A case study is provided to verify the superiority of delay and queue backlog performance through simulation results.

A Refined Collaborative Scheduling Method for Multi-Equipment at U-Shaped Automated Container Terminals Based on Rail Crane Process Optimization

A U-shaped automated container terminal (ACT) has been proposed for the first time globally and has been adopted to construct the Beibu Gulf Port ACT. In this ACT layout, the double cantilevered rail crane (DCRC) simultaneously provides loading and unloading services for the external container trucks (ECTs) and the automatic guided vehicles (AGVs) entering the yard. The DCRC has a complex scheduling coupling relationship with the AGV and the ECT, and its mathematical model is extremely complex. There is an urgent need to study a practical collaborative scheduling optimization model and algorithm for the DCRC, the AGV, and the ECT. In this paper, we optimize the process flow of DCRCs to study the refined collaborative scheduling model of DCRCs, AGVs and ECTs in U-shaped ACTs. Firstly, we analyze the operation process of the DCRC and divide the 16 loading and unloading conditions of the DCRC into four operation modes for process optimization. Secondly, different variables and parameters are set for the DCRC’s four operating modes, and a refined collaborative dispatching model for the DCRCs with AGVs and ECTs is proposed. Finally, a practical adaptive co-evolutionary genetic algorithm solves the model. Meanwhile, arithmetic examples verify the correctness and practicality of the model and algorithm. The experimental results show that the total running time of the DCRCs is the shortest in the U-shaped ACT when the number of quay cranes (QC) to DCRC and AGV ratios are 1:2 and 1:10, respectively. At the same time, the number of QCs and DCRCs has a more significant impact on the efficiency of the ACT than that of AGVs, and priority should be given to the allocation of QCs and DCRCs. The research results have essential guidance value for U-shaped ACTs under construction and enrich the theory and method of collaborative scheduling of U-shaped ACT equipment.

Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection

In order to alleviate the conflict at intersections in a networked environment between trams in a semi-exclusive right-of-way mode and social vehicles, the running characteristics of these trams and vehicles and their coupling characteristics with the intersection signal are analyzed. The positive and negative effects of the special priority signal on the intersection are considered, and a guidance optimization model that combines urban rails and roads is constructed. With the aim of creating an unplanned delay adjustment scenario for trams, an online collaborative optimization algorithm is proposed, which takes the energy consumption, passenger travel time and comfort of both trams and connected vehicles as optimization objectives in order to realize integrated adjustment of the running trajectories and signal timing, and the population is evolved by using an adaptive elitist genetic algorithm. The study is based on the actual traffic information and timing scheme of an intersection on the T1 line of the Sanya Tram. By comparing with conventional trajectories, the optimized trajectories reduce the total transit time by about 17.1%, the total energy consumption by about 34.7% and the passenger discomfort degree by about 27.8%. The contradiction of the right-of-way distribution at the intersection is alleviated, effectively safeguarding the interests of different users and enterprises. The experimental results show that the model has excellent applicability for different proportions of passenger numbers.

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters, a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed. At first, the objectives of satellite mission and optimization problems are clarified, and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit, payload, power and quality disciplines. In order to solve the problem of increasing non-linearity and coupling between these disciplines while using a standard collaborative optimization algorithm, an improved genetic algorithm is proposed and applied to system-level and discipline-level models. Finally, the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler (CSM) and MATLAB. The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible. It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

Autonomous and collaborative trajectory planning for traffic complexity management

AbstractTo accommodate growing air traffic demand, the traffic complexity management plays a crucial role in the capacity improvement of future automated air traffic management. In this paper, an autonomous and collaborative trajectory planning (ACTP) method is presented to facilitate collaborative decision‐making and enhance flexibility of the traffic complexity management in the en route airspace. A nonlinear integer model is formulated for the conflict‐free autonomous trajectory planning by reorganizing the trajectories with the combination of speed adjustment, lateral rerouting and flight level allocation maneuvers. Within the framework of the air‐ground situation awareness sharing, the spatio‐temporal reachable space is defined to describe the feasible solution space under the constraints of aircraft performance and required time of arrival. In addition, both the system‐level and the individual‐level objectives, traffic complexity and flight efficiency, are considered in the model, which are measured through the metric based on linear dynamical systems and the deviation from the user‐preferred trajectory. To balance the optimality and computational efficiency in the multi‐aircraft trajectory planning, a multi‐aircraft clustered and collaborative optimization algorithm is proposed based on the hybrid distributed‐centralized control strategy. The experimental results of the scenario in Western China airspace is presented, which verify the effectiveness of the proposed ACTP method through the comparison of other methods. Furthermore, a multi‐criteria decision‐making problem is discussed to strike a better trade‐off between different objectives in the trajectory planning.

A collaborative neurodynamic optimization algorithm to traveling salesman problem

This paper proposed a collaborative neurodynamic optimization (CNO) method to solve traveling salesman problem (TSP). First, we construct a Hopfield neural network (HNN) with n times n neurons for the n cities. Second, to ensure the convergence of continuous HNN (CHNN), we reformulate TSP to satisfy the convergence condition of CHNN and solve TSP by CHNN. Finally, a population of CHNNs is used to search for local optimal solutions of TSP and the globally optimal solution is obtained using particle swarm optimization. Experimental results show the effectiveness of the CNO approach for solving TSP.