Discrete Particle Swarm Optimization (DPSO) Algorithm for Permutation Flowshop Scheduling to Minimize Makespan

The booming cloud computing, together with edge computing, lays the computing resource foundation for larger and more granular simulation. To achieve better execution of the simulation in cloud and edge environments, it is necessary to assign simulation components to cloud computing centers and edge computing nodes appropriately. This is an optimization problem and a particle swarm optimization(PSO) algorithm is proposed for this purpose. However, deployment problems are a kind of discrete optimization problem and PSO algorithm was originally designed to solve continuous optimization problems, thus, various discretization methods had been proposed to modify original PSO algorithm to handle discrete problems. Unsatisfactorily, many discrete PSO algorithms suffer from random movements of particles due to improper use of the guidance information obtained from the global best particle and personal best particle. To counter this challenge, we proposed a selection-based discrete PSO algorithm(SPSO) and conducted simulation experiments on MATLAB platform. The results showed that SPSO got better performance in simulation components allocation problems compared with NSGA-II and other pure discrete PSO algorithms.

A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems

A high call blocking rate is a consequence of an inefficient utilization of system resources, which is often caused by a load imbalance in the network. Load imbalances are common in wireless networks with a large number of cellular users. This paper investigates a load-balancing scheme for mobile networks that optimizes cellular performance with constraints of physical resource limits and users quality of service demands. In order to efficiently utilize the system resources, an intelligent distributed antenna system (IDAS) fed by a multi base transceiver station (BTS) has the ability to distribute the system resources over a given geographic area. To enable load balancing among distributed antenna modules we dynamically allocate the remote antenna modules to the BTSs using an intelligent algorithm. A self-optimizing network for an IDAS is formulated as an integer based linear constrained optimization problem, which tries to balance the load among the BTSs. A discrete particle swarm optimization (DPSO) algorithm as an evolutionary algorithm is proposed to solve the optimization problem. The computational results of the DPSO algorithm demonstrate optimum performance for small-scale networks and near-optimum performance for large-scale networks. The DPSO algorithm is faster with marginally less complexity than an exhaustive search algorithm.

The wireless mesh network is a new emerging technology exploiting the advantages of multiple radios and multiple channels. In this paper we have studied the channel assignment problem which is to assign channels to links in the network, and aimed at minimizing the network interference so as to improve the network throughput. The channel assignment problem is proved to be NP-hard. In this paper, we have developed a new approach based on the particle swarm optimization (PSO) algorithm and a heuristic algorithm to solve channel assignment problem in multi-radio multi-channel wireless mesh networks. Meanwhile, the crossover operation is employed in the discrete particle swarm optimization (DPSO) algorithm to handle the discrete channel assignment problem. Experimental results have shown that the rate of interference in the DPSO algorithm descends by 67.67% compared with the Tabu-based algorithm when there are 12 channels and 5 radios. And the heuristic algorithm can also yield good solutions with good quality in both dense and sparse networks.

The storage location assignment of goods is critical to improve the efficiency of warehouse picking and distribution in the era of retail e-commerce. The optimization model of the storage location assignment problem (SLAP) for retail e-commerce is firstly established based on the principles of efficiency priority, shelf stability and similar products adjacency. Then, a new discrete particle swarm optimization (DPSO) algorithm is proposed to solve the NP-hard SLAP. In the DPSO, a new updating mechanism based on multi-fragment crossover and mutation operators is devised. Moreover, the elitist scheme and local search are incorporated into the DPSO to improve global search ability. Finally, five instances are used to compare the performance of the DPSO and state-of-the-art artificial fish swarm algorithm (AFSA). The computational results show that the DPSO is superior to the existing AFSA in solution quality.

With the continuous development of e-commerce, logistics and express services have penetrated into every aspect of people's life. Research on the optimization of logistics network model is helpful to reduce the waste of routes, improve the utilization rate of transportation tools and hubs, and thus reduce the organizational cost of logistics. In this paper, the basic model of hub-and-spoke network (HSN) is constructed based on the principle of minimizing the connection distance and total cost between hubs. By discretizing the particles in the continuous motion space, the discrete particle swarm optimization (PSO) algorithm is designed and the Exchange () exchange function is used to improve its search strategy to update the individual optimal value and the global optimal value. Finally, the improved double-layer discrete PSO algorithm is obtained to solve the logistics network model. The results show that the optimized PSO algorithm has faster convergence speed and higher precision, and the application of the logistics network model is helpful to integrate logistics resources, reduce logistics costs, and improve logistics efficiency.

A multi-objective discrete particle swarm optimization (MODPSO) algorithm is useful in accurately identifying communities in a network by avoiding the pitfalls of modularity optimized discrete PSO algorithms. Inertia weights in a PSO can be used to guide the flight of particles in PSO by modifying step size of the particles. In this paper, we present a new adaptive inertia weight based MODPSO and compare it with other good inertia weight approaches by applying them to three real-world datasets. Our algorithm demonstrates consistently the best results among various inertia weight strategies in three real-world datasets with maximum Q (modularity score) values of 0.457, 0.527728 and 0.60457 for Zachary’s Karate Club, Bottlenose Dolphins and American College Football datasets, respectively. Adaptive inertia weight strategy is able to perform consistently by adaptively determining the step size of the velocity update equation. To the best of our knowledge, this is the first such attempt to explore the adaptive inertia weight technique with MODPSO in the field of community detection in complex networks.

The signed network has received widespread attention because it can well reflect the cooperation and conflict relationship. Structural balance is an important global feature in signed networks, which can well reflect the structural characteristics of the network. Existing structural balance calculation algorithms define the global and local balance computation problems as an optimization problem, and then optimize their respective objective functions through optimization algorithms, but these algorithms ignore the correlation between the two problems. In this paper, we combine the multifactorial evolutionary algorithm and the discrete particle swarm optimization algorithm, and further propose the multifactorial discrete particle swarm optimization algorithm (MFDPSO). This algorithm designs the knowledge transfer function and optimization algorithm based on the correlation of the strong and weak structure balance and optimizes the two problems at the same time. The experimental results on 8 real networks demonstrate the effectiveness of the MFDPSO.

A kind of discrete Particle Swarm Optimization (PSO) algorithm named NDPSO was proposed for extending the classic PSO model to solve the discrete optimization problems with high effectiveness and stability. The concept of comparison with probability was also introduced based on NDPSO, and then the stochastic repairing operator was constructed for heuristic search to solve the Winner Determination Problem (WDP) in combinatory auction. The experimental results show that NDPSO has great advantages in both success rate and convergence speed compared with other discrete PSO algorithms and genetic algorithm.

With the rapid development of renewable energy, the proportion of small hydropower as a clean energy in the distribution network (DN) is increasing. However, the randomness and intermittence of small hydropower has brought new challenges to the operation of DN; especially, the problems of increasing network loss and reactive voltage exceeding the limit have become increasingly prominent. Aiming at the above problems, this paper proposes a reactive power optimization control method for DN with hydropower based on an improved discrete particle swarm optimization (PSO) algorithm. Firstly, this paper analyzes the specific characteristics of small hydropower and establishes its mathematical model. Secondly, considering the constraints of bus voltage and generator RP output, an extended minimum objective function for system power loss is established, with bus voltage violation serving as the penalty function. Then, in order to solve the following problems: that the traditional discrete PSO algorithm is easy to fall into local optimization and slow convergence, this paper proposes an improved discrete PSO algorithm, which improves the global search ability and convergence speed by introducing adaptive inertia weight. Finally, based on the IEEE-33 buses distribution system as an example, the simulation analysis shows that compared with GA optimization, the line loss can be reduced by 3.4% in the wet season and 13.6% in the dry season. Therefore, the proposed method can effectively reduce the network loss and improve the voltage quality, which verifies the effectiveness and superiority of the proposed method.

As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an emerging way to develop efficient solution algorithms for solving complex combinatorial optimization (CO) problems like machine scheduling problem. In this paper, we proposed an efficient optimization algorithm based on Deep RL for solving permutation flow-shop scheduling problem (PFSP) to minimize the maximum completion time. Firstly, a new deep neural network (PFSPNet) is designed for the PFSP to achieve the end-to-end output without limitation of problem sizes. Secondly, an actor-critic method of RL is used to train the PFSPNet without depending on the collection of high-quality labelled data. Thirdly, an improvement strategy is designed to refine the solution provided by the PFSPNet. Simulation results and statistical comparison show that the proposed optimization algorithm based on deep RL can obtain better results than the existing heuristics in similar computational time for solving the PFSP.

A discrete particle swarm optimization method for feature selection in binary classification problems

Layout optimization for underwater nozzle array of air-lifted artificial upwelling system based on discrete particle swarm algorithm

Network reconfiguration for loss reduction in a distribution system is a very important way to save the electrical energy. This article proposes a hybrid evolutionary algorithm to solve the distribution feeder reconfiguration problem. The algorithm combines a fuzzy adaptive particle swarm optimization with a differential evolution algorithm, called the fuzzy adaptive particle swarm optimization–differential evolution. The fuzzy adaptive particle swarm optimization includes two parts. The first part is fuzzy adaptive binary particle swarm optimization, which determines the status of tie switches (open or closed), and the second part is fuzzy adaptive discrete particle swarm optimization, which determines the sectionalizing switch number. In the proposed algorithm, due to the results of binary particle swarm optimization and discrete particle swarm optimization algorithms that highly depend on the values of their parameters (such as the inertia weight and learning factors), a fuzzy system is employed to adaptively adjust the parameters during the search process. The differential evolution algorithm is combined with the fuzzy adaptive particle swarm optimization algorithm to improve its performance. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and is guaranteed to obtain the global optimization.

For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization(DPSO) is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization(PSO), but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100?1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization(DPPSO) and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.