Discrete Particle Swarm Optimization Approach Research Articles

An improved discrete particle swarm optimization approach for a multi-objective optimization model of an urban logistics distribution network considering traffic congestion

To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a variable neighbourhood search strategy to improve the search ability of particles, which helps to improve the local search ability of the algorithm. Simulation results demonstrate the effectiveness of the proposed model in avoiding traffic congestion, reducing carbon emissions costs, and time penalty costs. The optimization comparison results between DPSO and PSO also verify the superiority of the DPSO algorithm. The proposed model can be applied to real-world urban logistics networks to improve their efficiency, reduce costs, and minimize environmental impact.

Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for short-term electricity price forecasting is proposed. In this work, we also establish an Inverted and Discrete Particle Swarm Optimization approach that is implemented for the optimization of the Variational Mode Decomposition method. The prediction of the day-ahead electricity prices is based on historical weather and price data of the deregulated Greek electricity market. The resulting forecasting outcomes are thoroughly compared in order to address which of the two proposed divide-and-conquer preprocessing approaches results in more accuracy concerning the issue of short-term electricity price forecasting. Finally, the proposed technique that produces the smallest error in the electricity price forecasting is based on Variational Mode Decomposition, which is optimized through the proposed variation of Particle Swarm Optimization, with a mean absolute percentage error value of 6.15%.

5G NOMA user grouping using discrete particle swarm optimization approach

Non-orthogonal multiple access (NOMA) technology meets the increasing demand for high-seed cellular networks such as 5G by offering more users to be accommodated at once in accessing the cellular and wireless network. Moreover, the current demand of cellular networks for enhanced user fairness, greater spectrum efficiency and improved sum capacity further increase the need for NOMA improvement. However, the incurred interference in implementing NOMA user grouping constitutes one of the major barriers in achieving high throughput in NOMA systems. Therefore, this paper presents a computationally lower user grouping approach based on discrete particle swarm intelligence in finding the best user-pairing for 5G NOMA networks and beyond. A discrete particle swarm optimization (DPSO) algorithm is designed and proposed as a promising scheme in performing the user-grouping mechanism. The performance of this proposed approach is measured and demonstrated to have comparable result against the existing state-of-the art approach.

A Multi-objective Swarm Intelligence Approach for Field Crews Patrol Optimization in Power Distribution Systems Restoration

A fault on a power distribution system may cause electricity interruption for several consumers, so a good restoration plan is required to decrease such interruptions duration and, consequently, assure the quality of service. Among the measures for service restoration, there is the dispatch of inspection and maintenance crews. The routing of these teams can be classified as a case of the multiple traveling salesman problem. Although involved in series of decision problems, the power distribution system maintenance crews routing is addressed, in the most part of the literature, as a single-objective problem, an instance of a multi-objective one, or as a multi-objective aggregating approach, which generates a single solution in an optimization run, in contrast with the set of equally good solutions, known as Pareto set, the result of a multi-objective problem. In this paper, a Pareto based multi-objective discrete particle swarm optimization approach was applied with the aim of reducing the patrol duration and also the total crews displacement. Wherein the concept of epsilon-dominance was used to update the set of non-dominated solutions, resulting in a good spreading and convergence of them. To promote an uniform exploration of the Pareto set, the selection of the local leaders of the archive was based on square root distance metrics. The Dijkstra algorithm was employed to find the shortest path between two consecutive points of the route of each team. As a result, a set of solutions were obtained for the routing of maintenance crews for power distribution system restoration.

Multiobjective Optimization for Arrangement of the Asymmetric-Paths Winding Based on Improved Discrete Particle Swarm Approach

The asymmetric-paths winding is a novel ac winding with a fractional ratio of the number of poles to the number of paths, which is beneficial for the optimized design of machines. Resulting from the numerous combinations of slot-vectors for path windings, the optimized arrangement with a low asymmetric degree can hardly be achieved by existing methods. Focusing on the optimization for arrangements of this winding, the mathematical optimization model considering two objectives for the percentage of the electromotive force error and the total harmonic distortion is built. In addition, an improved discrete particle swarm optimization approach is proposed and includes a setting method for the initial positions, a discrete operator with chaotic mutation for the velocity update, and an aggregate strategy combined with fuzzy inference. To validate the proposed approach, a winding design of a hydrogenerator is presented combined with the finite element analysis. The results show that the optimized arrangement presents a low asymmetric degree and harmonic. This approach provides a technical support for the arrangement optimization of an asymmetric-paths winding.

Discrete particle swarm optimization algorithms for two variants of the static data segment location problem

We consider the static data segment location problem in information networks. This problem was introduced by Sen et al. (Comput Oper Res, 62:282–295 2015). We consider the problem of optimally locating large volumes of digital content that is accessed via a distributed network. A database is pre-partitioned into multiple segments and the problem is one of placing these segments at servers located in different regions. We need to jointly consider four specific subproblems: (1) the problem of locating servers in the network, (2) the problem of allocating specific data segments to each of the servers, (3) the problem of assigning users to the servers based on their query patterns, and, (4) routing queries through the network. We consider two variants of this problem depending on the topology of the network through which the servers are connected: a mesh topology and a tree topology. In this paper, we develop a solution approach based on a discrete particle swarm optimization approach. We demonstrate the superiority of our approach by comparing its performance against solutions to benchmark instances obtained previously using a simulated annealing approach (Networks, 68(1):4–22 2016b).

Optimal rural land use allocation in central China: Linking the effect of spatiotemporal patterns and policy interventions

Rural land use development is experiencing a transition stage of socioeconomic and land use development in China. Historic land use transition process and policy interventions have key influence on the applicability of land use allocation solutions in future land use management. Strategic land use allocation is therefore required to possess a good adjustment capability to the transition process. Although heuristic optimization methods have been promising to solve land use allocation problems, most of them ignored the spatially explicit effect of historic land use transition and policies. To help resolve this issue, this study aims to optimize future land use pattern in the context of rural land use development. We took Yunmeng County, one of the typical major grain producing and rapidly urbanizing areas in central China, as a case study and solved the sustainable land use allocation problem by using an improved heuristic optimization model. The model was constructed based on the integration of a spatial discrete particle swarm optimization and cellular automata-Markov simulation approach. The spatiotemporal land use patterns and policy interventions were represented by the CA-Markov as in spatially explicit transition rules, and then incorporated into the discrete PSO for optimal land use solutions. We examined the influence of the joint effect of spatiotemporal land use patterns and policy interventions on the land use allocation outcome. Our results demonstrate the robustness and potential of the proposed model, and, more importantly, indicate the significance of incorporating the spatiotemporal land use patterns and policy interventions into rural land use allocation.

Discrete particle swarm optimization approach for cost sensitive attribute reduction

Attribute reduction is a key issue in rough set theory which is widely used to handle uncertain knowledge. However, most existing attribute reduction approaches focus on cost insensitive data. There are relatively few studies on cost sensitive data. Especially, how to evaluate a cost sensitive reduction algorithm is still an issue needing to be studied further. In this paper, we propose four relative evaluation metrics which can be used to compare and evaluate different algorithms for cost sensitive attribute reduction more conveniently. Moreover, we propose a particle swarm optimization method for cost sensitive attribute reduction problem inspired by its powerful search ability. The proposed approach is tested with three typical test cost distributions and compared with an influential algorithm reported recently on both exiting metrics and proposed metrics. Results indicate that the proposed relative evaluation metrics are effective and convenient. Comparing results also show that the proposed algorithm is effective.

Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks

This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the nondeterministic polynomial (NP) complete multicast routing problem (MRP). The main contribution is the extension of particle swarm optimization (PSO) from the continuous domain to the binary or discrete domain. First, a novel bi-velocity strategy is developed to represent the possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP, where 1 stands for a node being selected to construct the multicast tree, whereas 0 stands for being otherwise. Second, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in the continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the Operation Research Library (OR-library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly since it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on genetic algorithms, ant colony optimization, and PSO.

Distributed optimization Grid resource discovery

Grid computing is a framework for large-scale resource sharing and indexing that evolves with the goal of resource provisioning. In this paper, we develop a distributed learning automata (DLA) based on multi-swarm discrete particle swarm optimization (PSO) approach for Grid resource discovery, called distributed optimization grid (DOG) resource discovery algorithm. This algorithm makes use of swarms of particles for different computational resource metrics while a group of DLA is the control unit of each swarm of particles. The algorithm takes advantage of the PSO solution diversity to optimize the quality of delivered resource. Moreover, the recommended algorithm uses DLA as a fully distributed model for imitating the Grid infrastructure topology. Our experimental results show that DOG is fast as well as efficient and accurate.

Optimal Placement and Sizing of Capacitor and Power-Electronic Interfaced Distributed Generation in Heavy Harmonic Polluted Systems

Presence of distributed generation (DG) in distribution systems has significant impacts on the operational characteristics of these systems, also using capacitor for reactive compensation and loss reduction is so common. Injected harmonic currents from non-linear loads into distribution system distort all of voltages and currents and must be considered when placing the capacitor banks so that the resonance will not occur. Distributed Generation is often connected to the network via power-electronic interfaces for a proper coupling with the distribution networks. Inverters are capable of producing harmonic components and can be used as ancillary services for reducing harmonics by designing of a proper controlling system. In this paper discrete particle swarm optimization (DPSO) approach is used for the optimal placement and sizing of distributed generations and capacitors in distorted distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction. Constraints include voltage limit, voltage THD, number/ size of capacitors and generators. For evaluating the proposed algorithm, the IEEE 33-bus test system is modified and employed.

Study on the optimal design of frequency selective surfaces based on the discrete particle swarm optimization

The requirements of frequency-selective surface (FSS) between high transparency in pass band and high reflectance in stop band are contradictory, when they have loaded medium on one side and receive a large range of illumination. In order to solve the contradiction, this paper employs a discrete particle swarm optimization approach (hereafter referred to as a DPSO). In order to seek a balanced FSS with high transparency in pass band and high reflectance in stop band, the periodic intervals and geometrical dimensions of FSS-structures are optimized and designed by using the DPSO method. Simulation and test results indicate that the FSS of super dense Y loop elements in a half-loaded medium structure is presented in this paper: the transparency in pass band and stop band are 80% and 30% respectively. The DPSO method will offer an excellent FSS for the radome which receives a large range of illumination, and on the other hand, it provides a theoretical guidance for the requirements of FSS between high transparency in pass band and high reflectance in stop band.

Optimal Placement and Sizing of Capacitor and Distributed Generation with Harmonic and Resonance Considerations Using Discrete Particle Swarm Optimization

Presence of distributed generation (DG) in distribution systems has significant impacts on the operational characteristics of these systems, also using capacitor for reactive compensation and loss reduction is so common. Injected harmonic currents from non- linear loads into distribution system distort all of voltages and currents and must be considered when placing the capacitor banks so that the resonance will not occur. In this paper discrete particle swarm optimization (DPSO) approach is used for the optimal placement and sizing of distributed generations and capacitors in distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction. There is a term in the objective function which prevents harmonic resonance between capacitor reactance and system reactance. Constraints include voltage limit, voltage THD, number/ size of capacitors and generators. For evaluating the proposed algorithm, the IEEE 33-bus test system is modified and employed.

Optimizing the Vehicle Routing Problem With Time Windows: A Discrete Particle Swarm Optimization Approach

Vehicle routing problem with time windows (VRPTW) is a well-known NP-hard combinatorial optimization problem that is crucial for transportation and logistics systems. Even though the particle swarm optimization (PSO) algorithm is originally designed to solve continuous optimization problems, in this paper, we propose a set-based PSO to solve the discrete combinatorial optimization problem VRPTW (S-PSO-VRPTW). The general method of the S-PSO-VRPTW is to select an optimal subset out of the universal set by the use of the PSO framework. As the VRPTW can be defined as selecting an optimal subgraph out of the complete graph, the problem can be naturally solved by the proposed algorithm. The proposed S-PSO-VRPTW treats the discrete search space as an arc set of the complete graph that is defined by the nodes in the VRPTW and regards the candidate solution as a subset of arcs. Accordingly, the operators in the algorithm are defined on the set instead of the arithmetic operators in the original PSO algorithm. Besides, the process of position updating in the algorithm is constructive, during which the constraints of the VRPTW are considered and a time-oriented, nearest neighbor heuristic is used. A normalization method is introduced to handle the primary and secondary objectives of the VRPTW. The proposed S-PSO-VRPTW is tested on Solomon's benchmarks. Simulation results and comparisons illustrate the effectiveness and efficiency of the algorithm.

A Discrete Particle Swarm Optimization Approach to Optimize the Assembly Sequence of Mechanical Product

The aim of assembly sequence planning (ASP) is to achieve the best assembly sequence which assembly cost and time used is less. The geometrical feasibility of an assembly sequence is validated by the interference matrix of the product. The number of assembly tool changes and the number of assembly operation type changes are considered in the fitness function. To establish the mapping relation between ASP and particle swarm optimization (PSO) approach, some definitions of position, velocity and operator of particles are proposed. The difference of the proposed discrete PSO (DPSO) algorithm with the other algorithm is the emphasis on the geometrical feasibility of the assembly sequence. The geometrical feasibility is verified at the first and the every iteration. The performance and feasibility of the proposed algorithm is verified via a simplified engine assembly case.

Opposition-Based Discrete PSO Using Natural Encoding for Classification Rule Discovery

In this paper we present a new Discrete Particle Swarm Optimization approach to induce rules from discrete data. The proposed algorithm, called Opposition-based Natural Discrete PSO (ONDPSO), initializes its population by taking into account the discrete nature of the data. Particles are encoded using a Natural Encoding scheme. Each member of the population updates its position iteratively on the basis of a newly designed position update rule. Opposition-based learning is implemented in the optimization process. The encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attributes within the rule's antecedent to be a disjunction of the values of those attributes. The performance of the proposed algorithm is evaluated against seven different datasets using a tenfold testing scheme. The achieved median accuracy is compared against various evolutionary and non-evolutionary classification techniques. The algorithm produces promising results by creating highly accurate and precise rules for each dataset.

Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem

This paper presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-Sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The proposed DPSO approach is based on cost modification and uses PSO in its original form with continuous velocity equations. Each particle of the swarm is represented by a matrix of logistic costs. A sequential approach heuristic, using Wagner-Whitin algorithm, is used to determine the associated production planning. The authors demonstrate that any solution of the MLLP can be reached by particles. The sequential heuristic is a subjective function from the particles space to the set of the production plans, which meet the customer’s demand. The authors test the DPSO Scheme on benchmarks found in literature, more specifically the unique DPSO that has been developed to solve the MLLP.

A discrete particle swarm optimization approach for classification of Indian coal seams with respect to their spontaneous combustion susceptibility

Mine fires due to spontaneous combustion in coal mines is a global concern. This leads to serious environmental and safety hazards and considerable economic losses. Therefore it is essential to assess and classify the coal seams with respect to their proneness to spontaneous combustion to plan the production, storage and transportation capabilities in mines. This paper presents the formulation of clustering problem into a linear assignment model and the application of a discrete particle swarm optimization approach for the classification of coal seams based on their proneness to spontaneous combustion. In this research work, twenty nine coal samples of varying ranks belonging to both high and low susceptibilities to spontaneous combustion have been collected from all the major coalfields of India. Using moisture, volatile matter, and ash content and crossing point temperature of the coal samples as the parameters, the proposed algorithm has been applied to classify the coal seams into three different categories. This classification will be useful for the planners and field engineers for taking ameliorative measures in advance for preventing the occurrence of mine fires.

A particle swarm optimization approach for the bi-objective load balancing problem

Abstract We propose in this paper a two level loading problem that consists in packing items into containers, then stowing these containers in an aircraft while maximizing both of the total weight and the priority of the loaded cargo. At the same time, the center of gravity should be within a reasonable distance from the balance ideal position. We state the mathematical formulation of the problem. The minimization of the number of containers is tackled using a multi-objective placement heuristic and the loading process performs a discrete multi-objective particle swarm optimization approach. An experimental investigation is performed on various test instances to illustrate the effectiveness of our algorithm in solving the bi-objective loading problem.

An improved DPSO with mutation based on similarity algorithm for optimization of transmission lines loading

Static transmission network expansion planning (STNEP) problem acquires a principal role in power system planning and should be evaluated carefully. Up till now, various methods have been presented to solve the STNEP problem. But only in one of them, lines adequacy rate has been considered at the end of planning horizon and the problem has been optimized by discrete particle swarm optimization (DPSO). DPSO is a new population-based intelligence algorithm and exhibits good performance on solution of the large-scale, discrete and non-linear optimization problems like STNEP. However, during the running of the algorithm, the particles become more and more similar, and cluster into the best particle in the swarm, which make the swarm premature convergence around the local solution. In order to overcome these drawbacks and considering lines adequacy rate, in this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using an improved DPSO algorithm. The proposed improved DPSO is a new conception, collectivity, which is based on similarity between the particle and the current global best particle in the swarm that can prevent the premature convergence of DPSO around the local solution. The proposed method has been tested on the Garver’s network and a real transmission network in Iran, and compared with the DPSO based method for solution of the TNEP problem. The results show that the proposed improved DPSO based method by preventing the premature convergence is caused that with almost the same expansion costs, the network adequacy is increased considerably. Also, regarding the convergence curves of both methods, it can be seen that precision of the proposed algorithm for the solution of the STNEP problem is more than DPSO approach.