Hybrid Multi-objective Particle Swarm Optimization Research Articles

Multi-Objective Optimization of Circular Magnetic Couplers for Wireless Power Transfer Applications

Recent expansion of emerging wireless power transfer technology has not been followed by an adequate systematic approach to the magnetic couplers design. The relation between design parameters (mutual inductance, coupling coefficient, leakage field flux density, coil quality factor, and so on) and structural parameters (coils and ferrite shape, size, thickness, and so on) has not been thoroughly explored, and practical designs often depend on trial-and-error methods supported by the finite-element modeling simulation to verify a final design. In order to fill that gap, this paper describes the adoption of multi-objective hybrid particle swarm optimization (MOHPSO) and multi-objective real-numbered particle swarm optimization (MORPSO) algorithms for a circular coupler design to increase performances and automate the design process. Objectives of this paper are threefold: 1) apply MOHPSO and investigate if there are some unconventional structures of circular couplers capable of outperforming traditional designs; 2) identify a minimum set of key optimization parameters for a circular magnetic coupler; and 3) demonstrate the operation of an MORPSO algorithm to optimize a circular magnetic coupler. The Pareto front concept is applied to provide multi-objective optimization. Two different multi-objective function pairs are considered, the first pair being the coil coupling coefficient ( $ {k}$ ) and maximum leakage field magnetic flux density ( $ {B_{\max }}$ ); for the second pair, the product between quality factor ( $ {Q}$ ) and $ {k}$ is combined with ( $ {B_{\max }}$ ). To validate the optimization algorithm effectiveness and accuracy, a selected magnetic coupler is fabricated and tested. Due to superior execution time, and satisfactory optimization capabilities, the MORPSO algorithm is employed to generate a final optimum design. Collected measurements demonstrate a very good agreement between the experimental and simulation results.

Optimal Allocation of Energy Storage System Considering Multi-Correlated Wind Farms

With the increasing penetration of wind power, not only the uncertainties but also the correlation among the wind farms should be considered in the power system analysis. In this paper, Clayton-Copula method is developed to model the multiple correlated wind distribution and a new point estimation method (PEM) is proposed to discretize the multi-correlated wind distribution. Furthermore, combining the proposed modeling and discretizing method with Hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), a comprehensive algorithm is explored to minimize the power system cost and the emissions by searching the best placements and sizes of energy storage system (ESS) considering wind power uncertainties in multi-correlated wind farms. In addition, the variations of load are also taken into account. The IEEE 57-bus system is adopted to perform case studies using the proposed approach. The results clearly demonstrate the effectiveness of the proposed algorithm in determining the optimal storage allocations considering multi-correlated wind farms.

Voltage-based reactive power pricing in deregulated environment using hybrid multi-objective particle swarm optimisation

ABSTRACTReactive power market management along with voltage stability is needed to maintain system reliability and to meet the required security levels in deregulated power system. This paper explores the technical and economic issues of reactive power pricing mechanism along with improvement in voltage stability in deregulated system. The algorithm proposes a multi-objective optimisation problem for minimising reactive power pricing and cost of energy loss along with voltage stability improvement using Hybrid Multi-Objective Particle Swarm Optimization . The voltage magnitude of generators, reactive power settings of shunt capacitors and the transformer tap settings are taken as the control variables. The proposed problem uses a well-defined reactive power-bid structure based on long-term capacity bids provided by the generators and capacitors to the system operator to ensure voltage security. The proposed cost-based pricing strategy along with stability enhancement has been implemented in IEEE 30-bus test system under normal and stressed system conditions.

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling

Now-a-days, Cloud computing is a technology which eludes provision cost while providing scalability and elasticity to accessible resources on a pay-per-use basis. To satisfy the increasing demand of the computing power to execute large scale scientific workflow applications, workflow scheduling is the main challenging issue in Infrastructure-as-a-Service (IaaS) clouds. As workflow scheduling belongs to NP-complete problem, so, meta-heuristic approaches are more preferred option. Users often specified deadline and budget constraint for scheduling these workflow applications over cloud resources. But these constraints are in conflict with each other, i.e., the cheaper resources are slow as compared to the expensive resources. Most of the existing studies try to optimize only one of the objectives, i.e., either time minimization or cost minimization under user specified Quality of Service (QoS) constraints. But due to the complexity of workflows and dynamic nature of cloud, a trade-off solution is required to make a balance between execution time and processing cost. To address these issues, this paper presents a non-dominance sort based Hybrid Particle Swarm Optimization (HPSO) algorithm to handle the workflow scheduling problem with multiple conflicting objective functions on IaaS clouds. The proposed algorithm is a hybrid of our previously proposed Budget and Deadline constrained Heterogeneous Earliest Finish Time (BDHEFT) algorithm and multi-objective PSO. The HPSO heuristic tries to optimize two conflicting objectives, namely, makespan and cost under the deadline and budget constraints. Along with these two conflicting objectives, energy consumed of created workflow schedule is also minimized. The proposed algorithm gives a set of Pareto Optimal solutions from which the user can choose the best solution. The performance of proposed heuristic is compared with state-of-art multi-objective meta-heuristics like NSGA-II, MOPSO, and ε-FDPSO. The simulation analysis substantiates that the solutions obtained with proposed heuristic deliver better convergence and uniform spacing among the solutions as compared to others. Hence it is applicable to solve a wide class of multi-objective optimization problems for scheduling scientific workflows over IaaS clouds.

Multi-objective and multi-level image thresholding based on dominance and diversity criteria

The Kapur and Otsu methods are widely used image thresholding approaches and they are very efficient in bi-level thresholding applications. Evolutionary algorithms have been developed to extend the Kapur and Otsu methods to the multi-level thresholding case. However, there remains an unsolved argument that neither Kapur nor Otsu objective can optimally fit diverse content contained in different kinds of images. This paper proposes a multi-objective model which seeks to find the Pareto-optimal set with respect to Kapur and Otsu objectives. Based on dominance and diversity criteria, we developed a hybrid multi-objective particle swarm optimization (MOPSO) method by incorporating several intelligent search strategies. The ensemble strategy is also applied to automatically select the best search strategy to perform at various algorithm stages according to its historic performances. The experimental result shows that the solutions to our multi-objective model consistently produce equal or better segmentation results than those by the optimal solutions to the original Kapur and Otsu models, and that the proposed hybrid algorithm with and without the ensemble strategy produces a better approximation to the ideal Pareto front than those obtained by two other MOPSO variants and the MOEA/D. In comparison with the most recent multilevel thresholding methods, our approach also consistently obtains better performance in the segmentation result for several benchmark images.

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA

A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm.

An innovative hybrid multi-objective particle swarm optimization with or without constraints handling

This paper presents a new hybrid optimizer in which an innovative optimal particles local search strategy on basis of bound optimization by quadratic approximation (BOBYQA) algorithm and exterior penalty function method is integrated into particle swarm optimization (PSO). The main goal of the approach is to improve the convergence performance of PSO, and preserve the diversity of non-dominated set. Our algorithm selects some non-dominated solutions lied in less-crowded region of external archive based upon crowding distance value to construct a leader particles set, and make full use of optimal particles method to guide leader particles approach the Pareto front quickly. Meanwhile, a local optimal particles search strategy is proposed after particular analysis on disadvantage of global optimal particle search method, and names our algorithm as LOPMOPSO. Furthermore, the multi-dimensional uniform mutation operator is performed to prevent algorithm from trapping into local optimum, and a dynamic archive maintenance strategy is applied to improve the diversity of solutions. For coping with the constrained conditions consists in objective functions, we adopt an efficient infeasibility degree evaluation criterion to deal with these complex problems. Simulation results of various kinds of benchmark functions show that our approach is highly competitive in convergence speed and generates a well distributed and accurate set of non-dominated solutions easily. The solving of a 2-D aerodynamic optimization problem further validates its speed and effectiveness.

Hybrid Multi-Objective Particle Swarm Optimization for Flexible Job Shop Scheduling Problem

Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.

Economic Allocation for Energy Storage System Considering Wind Power Distribution

Energy storage systems play a significant role in both distributed power systems and utility power systems. Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems. Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production. Furthermore, the probability cost analysis is first put forward in this paper. The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique. It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution. The IEEE 30-bus system is adopted to perform case studies. The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method.

An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search

Recently more research works are focused on multi-objective particle swarm optimization algorithm (MOPSO) due to its ability of global and local search for solving multi-objective optimization problems (MOOPs); however, most of existing MOPSOs cannot achieve satisfactory results in solution quality. This paper proposes an efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search (MOLS), named MOLS-MOPSO, to deal with such problem. MOLS-MOPSO combines an effective particle updating strategy with the local search of MOLS. The effective particle updating strategy is used for global search to deal with premature convergence and diversity maintenance within the swarm; the MOLS is periodically activated for fast local search to converge toward the Pareto front. The exploratory capabilities are enhanced more efficiently by keeping a desirable balance between global search and local search, so as to ensure sufficient diversity and well distribution amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front. Comparing MOLS-MOPSO with various state-of-the-art multi-objective optimization algorithms developed recently, the comparative study shows the effectiveness of MOLS-MOPSO, which not only assures a better convergence to the Pareto frontier but also illustrates a good diversity and distribution of solutions.

Research on Virtual Cellular Manufacturing Scheduling Based on the Scale-Free Random Network Model

This paper studies the resource conflict resolution problem for virtual cellular manufacturing inter-cell scheduling. The existing literatures on resource conflict of inter-cell scheduling don't take account into delay within building a model. They try to resolve conflict according to coordination strategy, but it is always accompanied by delay. In view of this deficiency and new characteristics of virtual cellular inter-cell scheduling problem, we consider the tardiness of all products. A hybrid multi-objective particle swarm optimization algorithm is proposed, and considers task priority of shared resource. A new decoding method which simulates market mechanism solves resource conflict problem. The feasible task scheduling shortens total flow time and tardiness, improves the utilization of shared resource. Finally, an example is used to verify its feasibility and validity.

The Resource Conflict Resolution Problem for Virtual Cellular Manufacturing Inter-Cell Scheduling under the Conditions of Resource Sharing

This paper studies the resource conflict resolution problem for virtual cellular manufacturing inter-cell scheduling. The existing literatures on resource conflict of inter-cell scheduling don't take account into delay within building a model.They try to resolve conflict according to coordination strategy, but it is always accompanied by delay. In view of this deficiency and new characteristics of virtual cellular inter-cell scheduling problem, we consider the tardiness of all products. A hybrid multi-objective particle swarm optimization algorithm is proposed, and considers task priority of shared resource. A new decoding method which simulates market mechanism solves resource conflict problem. The feasible task scheduling shortens total flow time and tardiness, improves the utilization of shared resource. Finally, an example is used to verify its feasibility and validity.

Application of Hybrid MOPSO Algorithm to Optimal Reactive Power Dispatch Problem Considering Voltage Stability

This study presents a novel hybrid multiobjective particle swarm optimization (HMOPSO) algorithm to solve the optimal reactive power dispatch (ORPD) problem. This problem is formulated as a challenging nonlinear constrained multiobjective optimization problem considering three objectives, that is, power losses minimization, voltage profile improvement, and voltage stability enhancement simultaneously. In order to attain better convergence and diversity, this work presents the use of combing the classical MOPSO with Gaussian probability distribution, chaotic sequences, dynamic crowding distance, and self-adaptive mutation operator. Moreover, multiple effective strategies, such as mixed-variable handling approach, constraint handling technique, and stopping criteria, are employed. The effectiveness of the proposed algorithm for solving the ORPD problem is validated on the standard IEEE 30-bus and IEEE 118-bus systems under nominal and contingency states. The obtained results are compared with classical MOPSO, nondominated sorting genetic algorithm (NSGA-II), multiobjective evolutionary algorithm based on decomposition (MOEA/D), and other methods recently reported in the literature from the point of view of Pareto fronts, extreme, solutions and multiobjective performance metrics. The numerical results demonstrate the superiority of the proposed HMOPSO in solving the ORPD problem while strictly satisfying all the constraints.

A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

A bi-objective optimization of supply chain design and distribution operations using non-dominated sorting algorithm: A case study

This paper considers simultaneous optimization of strategic design and distribution decisions for three-echelon supply chain architecture consisting of following three players; suppliers, production plants, and distribution centers (DCs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to distribution centers, so as to minimize the combined facility location, production, inventory, and shipment costs and maximize fill rate. To achieve this, three-echelon network model is mathematically represented and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization algorithm (MOHPSO). This heuristic incorporates non-dominated sorting (NDS) procedure to achieve bi-objective optimization of two conflicting objectives. The applicability of proposed optimization algorithm was then tested by applying it to standard test problems found in literature. On achieving comparable results, the approach was applied to actual data of a pump manufacturing industry. The results show that the proposed solution approach performs efficiently.

Application of Response Surface Methodology and Elitist Multi-objective Hybrid Particle Swarm Algorithm for Optimization Design of an Air-Core Linear Motor

The main purpose of this study was to find out the optimal design variables of an air-core linear brushless permanent magnet motor (LBPMM) by simultaneously considering the maximal thrust, minimal temperature, and minimal volume. Airgap length, magnet dimensions (magnet height and magnet width), and coil winding height were chosen as design variables in this multi-objective optimization problem. Using response surface methodology (RSM), this study developed a mathematical predictive model for each of the objectives. A multi-objective hybrid particle swarm optimization (PSO) with a mutation operator and a dynamic inertia weight factor was used to optimize the model developed by RSM. In addition, an elitist mechanism with crowding distance sorting was used to improve the correctness and diversity of the solutions. The results show that the Pareto-optimal front solutions of the proposed approach, providing the designers with more design plans, are superior to those of non-dominated sorting genetic algorithm ( NSGA II ). Designers can expand the effective method for designing linear motors to successfully solve more complex problems in other designed components.

Bi‐objective optimization of distribution scheduling using MOPSO optimizer

PurposeThe paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.Design/methodology/approachTo optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.FindingsThis paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.Originality/valueVery few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

Location and allocation decisions for multi-echelon supply chain network – A multi-objective evolutionary approach

This paper aims at multi-objective optimization of single-product for four-echelon supply chain architecture consisting of suppliers, production plants, distribution centers (DCs) and customer zones (CZs). The key design decisions considered are: the number and location of plants in the system, the flow of raw materials from suppliers to plants, the quantity of products to be shipped from plants to DCs, from DCs to CZs so as to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met. To optimize these two objectives simultaneously, four-echelon network model is mathematically represented considering the associated constraints, capacity, production and shipment costs and solved using swarm intelligence based Multi-objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. This evolutionary based algorithm incorporates non-dominated sorting algorithm into particle swarm optimization so as to allow this heuristic to optimize two objective functions simultaneously. This can be used as decision support system for location of facilities, allocation of demand points and monitoring of material flow for four-echelon supply chain network.

Power system stabilizer design using hybrid multi-objective particle swarm optimization with chaos

A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed, by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC). Firstly, a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC). It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search. Secondly, the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima. The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions. The performance of the proposed MPSOC was compared to the MPSO, PSO and GA through eigenvalue analysis, nonlinear time-domain simulation and statistical tests. Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs. The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA, PSO and MPSO.