Optimization of green agri-food supply chain network using chaotic PSO algorithm

The green agricultural products supply chain network (GAP-SCN) design is to provide an optimal platform for efficient and effective supply chain management. This paper proposes a new solution based on chaotic genetic algorithm (CGA) to find optimal solution for the GAP-SCN problem. Different from other methods in the literature, CGA adopts transforming operator to modify chromosomes in the population, uses the blending operator of roulette wheel selection and elitist reserve strategy and uses the chaotic operator to enhance diversity of chromosomes in order to avoid populations trapping in local optima. The novelty of the transforming operator is that it can avoid applying the penalty function so that the diversity of populations is decreased. To show the efficacy of the algorithm, CGA is also tested on three cases. Results show that the proposed algorithm is promising and outperforms the classic GA by both optimization speed and solution quality.

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles’ search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles’ premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO.

An improved Chaotic Particle Swarm Optimization (CPSO) algorithm for a jobshop scheduling problem, with minimization of makespan as the criterion, is proposed in this research. A real-valued encoding scheme based on a matrix representation is developed, which converts the continuous position value of particles in PSO to the processing order of job operation. A compound chaotic search strategy that integrates both Tent and Logistic chaotic search process is employed to the global best particle to enhance the local searching ability of PSO. In addition, a gaussian disturbance technology is embedded in the CPSO algorithm to improve the diversity of the particles in the swarm. The performance of CPSO is compared with the standard PSO algorithm on a benchmark instance of jobshop scheduling problems. The results show that the proposed CPSO algorithm has a superior performance to the PSO algorithm.

Determination of voltage stability boundary values in electrical power systems by using the Chaotic Particle Swarm Optimization algorithm

In this paper, integrated preventive maintenance planning and production scheduling for a single machine is formulated as a multi-objective mathematical optimization problem. Despite the inter-dependent relationship between them, production scheduling and preventive maintenance planning decisions are generally analyzed and executed independently in real manufacturing systems. This practice is also found in the majority of the studies found in the relevant literature. The chaotic particle swarm optimization (CPSO) algorithm is proposed to solve the integrated optimization model for production scheduling and preventive maintenance planning. The numerical results on several problem sizes indicate that the methodology's feasibility and efficiency

Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm

Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

Abstract- The goal of navigating a mobile robot is to find the optimal path to direct its movement, so path planning is the best solution to find the optimal path. Therefore, the two most important problems of path planning must be solved; the first is that the path must avoid collision with obstacles, and second it must reduce the length of the path to a minimum. This paper will discuss finding the shortest path with the optimum cost function by using the Chaotic Particle Swarm Optimization (CPSO), and A*, compare the results between them and the proposed hybrid algorithm that combines A* and Chaotic Particle Swarm Optimization (ACPSO) algorithms to enhance A* algorithm to find the optimal path and velocities of the wheeled mobile robot. These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the proposed algorithm in terms of minimum number of an evaluation function and the shortest path length as well as to obtain the optimal or near optimal wheel velocities. Index Terms—Mobile robot, Path planning, Chaotic Particle Swarm Optimization (CPSO), A* algorithm, Fixed obstacles.

The application of particle swarm optimization algorithm in underwater wireless sensor node deployment strategy was studied. The chaotic particle swarm optimization algorithm was proposed. Set up a function variance to determine whether the particles enter the premature state. Then chaos searched and took the chaotic search optimal solution vector as the global optimization solution to guide the particles quickly jump out of local optimum, accelerate the convergence speed and to overcome the premature problem of standard particle swarm algorithm brought. The network coverage was the fitness function to optimize the deployment of nodes question. The chaotic particle swarm optimization algorithm was used to wireless sensor network node deployment strategy. The experimental results showed that this method could effectively improve the convergence speed and increase network coverage.

PurposeThe purpose of this paper is to develop research frameworks for two types of green supply chains based on Fisher's seminal work. In spite of Fisher's contribution to the literature, his study has rarely been extended into green supply chain contexts, except in a few conceptual arguments. The current study explores how Fisher's perspective of efficient versus responsive supply chains can be a stepping stone to the development of two green supply chains: eco‐efficient and eco‐responsive supply chains.Design/methodology/approachToward the above end, a case methodology is employed (Yin). Two Korean global companies, Pohang Iron and Steel Company (POSCO) and Samsung Electronics (SEC), are selected to explore eco‐efficient and eco‐responsive supply chains. POSCO (steel products) is selected to enable the understanding of how efficient and eco‐efficient supply chains work. SEC (mobile phone products) is chosen to understand responsive and eco‐responsive supply chains.FindingsThe findings suggest that POSCO tends to stress process technology innovation as a means to address green pressures, while SEC accentuates the green product strategy; while an eco‐efficient supply chain focuses on keeping to an environmental standard across the supply chains, an eco‐responsive supply chain centers on the collaboration of suppliers and distributors in greening the supply chain; and SEC takes more initiatives to educate and encourage consumers to engage in recycling activities than POSCO does.Originality/valueFew studies have examined the green supply chain using the perspective of Fisher's framework (efficient vs responsive supply chains). By addressing the timely topic, this study fills a research gap in green supply chain literature.

Supply chain management is a key component of the Electronic-China Yuan (e-CNY) infrastructure and is crucial to developing and using e-CNY. This paper first provides an overview of the features of e-CNY and the idea of incorporating supply chain management into the creation of e-CNY software to better meet user payment requirements. This will help to reinforce the CNY's dominant position in the global monetary system. Meanwhile, this paper uses the fuzzy theory evaluation method to assess the software supply chain management informatization level. More broadly, this paper discusses the development level of digital enterprise supply chain management informatization to understand the current situation of e-CNY software supply chain management. The network's energy usage will increase when e-CNY software is developed. This research suggests a routing protocol based on the combination of chaotic particle swarm optimization (CPSO) and ant colony algorithm (ACA). It employs a new CPSO algorithm to optimize the cluster head selection.

A new hybrid model combining evolutionary artificial neural network (EANN) and mathematical models (MM) is proposed to improve the prediction precision of flow stress of 45 steel. In EANN, the optimal parameters are obtained by chaotic particle swarm optimization (CPSO) algorithm. CPSO adopts chaotic mapping and combines local search and global search, possessing high search efficiency and good performance. The results obtained from the computational study have shown that the proposed model can correctly recur to the flow stress in the sampled data and it can also predict well the non-sampled data. The efficiency and accuracy of the proposed model are demonstrated in comparison with the model combining BP networks with mathematical models (BPN-MM) used in much literature.

A strategic model for supply chain design with logical constraints: formulation and solution

In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: Green supply chain management, multi-objective modelling, network optimization;

Aiming at the EEG signal radio frequency control problem of EEG signal radio control wheeled robot traversing multiple objective points, a new method of EEG signal radio frequency control that integrates the bi-objective chaotic particle swarm optimization (BCPSO) is put forward. This method transforms the selection of objective points into the EEG signal radio frequency control problem and makes optimization by using the ant colony algorithm. The EEG signal control function between the two objective points is defined, and the particle swarm optimization algorithm is applied for optimization. Given the premature phenomenon of particle swarm optimization algorithm, the inverse learning strategy is introduced into the particle swarm optimization algorithm, and the inertia weight and learning factor of the particle swarm optimization algorithm are improved. The performance test results show that the bi-objective chaotic particle swarm optimization algorithm can effectively prevent the premature particle phenomenon and improve the optimization capacity and stability of the particle swarm optimization algorithm.