Multiobjective particle swarm optimization for optimal power flow problem

In this paper, a multi-objective hybrid firefly and particle swarm optimization (MOHFPSO) was proposed for different multi-objective optimal power flow (MOOPF) problems. Optimal power flow (OPF) was formulated as a non-linear problem with various objectives and constraints. Pareto optimal front was obtained by using non-dominated sorting and crowding distance methods. Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. The efficiency of the proposed MOHFPSO technique was tested on standard IEEE 30-bus and IEEE 57-bus test systems with various conflicting objectives. Simulation results were also compared with non-dominated sorting based multi-objective particle swarm optimization (MOPSO) and different optimization algorithms reported in the current literature. The achieved results revealed the potential of the proposed algorithm for MOOPF problems.

As the world is progressing towards faster and more efficient computing techniques, cloud computing has emerged as an efficient and cheaper solution to such increasing and demanding requirements. Cloud computing is a computing model which facilitates not only the end-users but also organizational and other enterprise users with high availability of resources on demand basis. This involves the use of scientific workflows that require large amount of data processing, which can be costly and time-consuming if not properly scheduled in cloud environment. Various scheduling strategies have been developed, which include swarm-based optimization approaches as well. Due to the presence of multiple and conflicting requirements of users, multi-objective optimization techniques have become popular for workflow scheduling. This paper deals with cat swarm-based multi-objective optimization approach to schedule workflows in a cloud computing environment. The objectives considered are minimization of cost, makespan and CPU idle time. Proposed technique gives improved performance, compared with multi-objective particle swarm optimization (MOPSO) technique.

As far as continuous supply and reliable power delivery to the distribution system customers are concerned, finding a practical and cost-effective method for locating faults after their occurrence is of high importance. In this study, a method is proposed to determine the optimal number and location of power quality monitors (PQMs) to make the distribution network fault-observable, that means, to be able to locate faults as precisely as possible. Moreover, as placement of PQMs is highly dependent on the network topology, the proposed method considers the most probable configurations of the network for optimization. The error of measuring equipment and its effect on number of PQMs is also taken into consideration. The defined objective functions of this study aim to minimize the cost of installing PQMs while minimizing the number of blind-pairs and maximizing the fault-observability level of the network. These objective functions are optimized using Multi-Objective Particle Swarm Optimization (MOPSO) technique. Additionally, to have a better economic evaluation, two scenarios are defined based on the accuracy class of monitoring equipment. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 123-bus distribution test system.

In this brief, we deal with the generation of the Pareto front for multi-objective analog circuit sizing optimization. The main idea of the proposed work consists of using metamodels of the considered performances to generate the set of nondominated solutions. These models serve as evaluators. They offer several advantages, mainly their high precision for reproducing the real behavior of the considered performances, and their very rapid evaluation, when compared to their counterpart, i.e. the in-loop-based sizing technique. Multiobjective particle swarm optimization (MOPSO) technique is used as the optimization metaheuristic. The proposed approach is first applied to some mathematical test functions, then, we consider the optimal sizing of MOS current conveyors, where two conflicting objectives are considered, namely maximizing the current transfer cutoff frequency (fci) and minimizing the parasitic X-port resistance (Rx). Obtained results are compared to those reached using the conventional in-loop optimization technique. Hspice simulations, using AMS 0.35μm CMOS technology, are given to highlight these reached results.

In this paper, a simultaneous calibration algorithm of the parameters of the attitude and altitude control for an unmanned aerial vehicle (UAV) is proposed. The algorithm is based on the multi-objective particle swarm optimization (MOPSO) technique. This algorithm is implemented by using the free PX4 software for the Pixhawk2 controller. The behavior of the UAV is simulated given its physical characteristics by means of a non-linear model and a search of the controller parameters. This latter is based on a proportional (P) position controller in cascade with a proportional-integral-derivative (PID) speed controller of its height and each one of its Euler angles. To perform this search, the PID gains K p1 , K p2 , K i and K d of each of the degrees of freedom are used to define vectors considered particle positions by the MOPSO algorithm, which moves them through a search space to find sets of optimum values according to Pareto, or the Pareto Front. The search is carried out based exclusively on Pareto dominance concepts, comparing parameters of step responses (overshoot, rise time, root-mean-square error) of each of the degrees of freedom. In order to show the efficiency of the proposal, simulation results are provided by using the calibration methodology obtaining good results.

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

Reliability-based performance simulation for optimized pavement maintenance

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Comparison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

Hydrogen–Fuel Cell (HFC) systems have become one of the most promising zero-carbon shipping solutions, with high energy-conversion efficiency, silent operation and potential for full decarbonization when coupled with renewable hydrogen production. Yet, the operational efficiency of HFC marine power systems is significantly affected by nonlinear electrochemical kinetics, interactions between subsystems and extremely wide fluctuating load profiles typical for naval service missions. The resulting complex multi-variable control laws open up trade-offs between efficiency, hydrogen consumption, dynamic response and emission of auxiliary by-products like NOx, unconverted hydrogen and water vapor exhaust. To tackle these issues, the present work puts forward a complete Multi Objective Particle Swarm Optimization (MOPSO) framework for the combined performance and emissions optimization of hydrogen marine–FC propulsion systems. The MOPSO technique is used to optimize decision variables such as fuel cell operating current density, stack temperature, air stoichiometric ratio, compressor control and the proportion at which ESS is lead on hybrid with a Pareto-optimal trade-offs between: (i) system efficiency, (ii) hydrogen consumption, (iii) emissions and by-product reduction, and (iv) component degradation and thermal stress reduction. A high-fidelity dynamic simulation model has been developed, combining PEM fuel cell electrochemical kinetics, onboard power demand information and compressor-humidifier dynamics with marine propulsion load cycles for patrol, ferry and tugboat duties. Results show that the MOPSO optimized control strategy increases net fuel cell efficiency by 12-18%, decreases hydrogen consumption by 15-27%, and decreases auxiliary emissions and stack temperature excursions by up to 35% as compared with the baseline, rule based, and single-objective tuning approaches. Moreover, degradation parameters such as voltage decay rate and membrane hydration instability were dramatically mitigated which enabled extension of the projected stack life time by 20–30%.

The Vertical bearing offset is vital for the safe and efficient operation of marine shafting systems. The bearing offset is made relative to the shaft and must be tested for various operational conditions to prevent overheating, whipping, vibration, and wear that can lead to costly repairs and downtime. The offset of each bearing is achieved by minute adjustment of the bearing pedestal performed during dry-docking. However, the hull load variation can cause the preset bearing offsets to be inadequate resulting in potential bearing damage. Classification Society of ABS and DNV specifies the limit of bearing reaction force, shaft slope, and bending moment to ensure safe operation. These values are usually determined using a finite element model or experimentally using costly strain gauges. A more cost-effective solution is using the stiffness matrix of the bearing shaft system to derive the influence coefficients for the bearing reaction forces (BIC), shaft slope (SIC), and bending moment (MIC). This approach is validated with the existing model of a marine shafting system of a 50 DWT oil tanker that consists of one tail and intermediate shaft supported by stern tube bearing (STB), and intermediate bearing (IB) respectively along with six bearings at the engine shaft using the finite element method. Besides, the study applies the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimize the vertical position of bearings. It uses BIC, Slope SIC, and MIC to construct the objective functions and find a Pareto-optimal solution that satisfies the requirements of ABS and DNV in terms of bearing reaction forces, bending moments, and slope at the main stern tube and intermediate bearing. The optimal bearing offsets show improved shaft bending state for the operating condition under evaluation and reduced the slope at the STB by 34%, indicating its effectiveness in solving optimization problems.

This paper presents a new approach for environmental/economic transaction planning model of the electricity market. The environmental/economic transaction planning problem is formulated as a multi-objective optimal power flow (MOPF) problem. A novel algorithm using multiobjective particle swarm optimization (MOPSO) and non-stationary multi-stage assignment penalty function is proposed to solve this problem. PSO is modified by using dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Incorporating of non-stationary multistage assignment penalty function in solving OPF problem can improve the convergence. The proposed method is demonstrated on the IEEE 30-bus system. The results show that the proposed approach can efficiently gain multiple pareto optimal transaction planning that match with the sustainable development strategy

In this chapter, a new multi-objective blended particle swarm optimization (MOBPSO) technique is proposed for the selection of significant and informative genes from the cancer datasets. As the basic optimization algorithm suffers from the local trapping, a blended Laplacian operator is integrated with it to overcome the drawback. The concept is also implemented for differential evolution, artificial bee colony, genetic algorithm and subsequently multi-objective blended differential evolution (MOBDE), multi-objective blended artificial bee colony (MOBABC) and multi-objective blended genetic algorithm (MOBGA) are proposed to extract the relevant genes from the cancer datasets. Proposed methodology utilizes two objective functions to sort out the genes which are differentially expressed from class to class as well as provides good results for the classification of disease. Experimental result reveals that the proposed methodology very efficiently selects differential and biologically relevant genes which are effective for the classification of disease which in turn offers more useful information about the gene–disease association.

The optimal power flow (OPF) problem is a well-known non-convex optimization problem that aims to minimize the cost of electric power generation subject to consumer demand, the physics of power flow, and technological constraints. To find an optimal solution to this problem, local search techniques such as interior point methods are typically used. However, due to the non-convex nature of the problem, these methods are likely to result in a sub-optimal solution. The goal of this paper is to characterize the worst-case performance of local search on the OPF problem. To accomplish this, we formulate the OPF problem as a canonical quadratically-constrained quadratic program (QCQP). Then, we study the problem of finding the worst-case local minimum of this QCQP, which is non-convex and hard to solve in general. We find a relaxation of this problem into a semidefinite program (SDP) and show that it is exact for certain cases. Using some test cases which are known to have multiple local minima, we demonstrate the effectiveness of the proposed relaxation to bound the worst-case local minimum. We compare the obtained upper bound on local minima to the lower bound provided by the standard SDP relaxation of the OPF problem to understand how much SDP outperforms local search for a given problem.

Recent years have witnessed the success of employing convex relaxations of the AC optimal power flow (OPF) problem to find global or near-global optimal solutions. The majority of the effort has focused on solving problem formulations where variables live in continuous spaces. Our focus here is in the extension of these results to the co-optimization of network topology and the OPF problem. We employ binary variables to model topology reconfiguration in the standard semidefinite programming (SDP) formulation of the OPF problem. This makes the problem non-convex, not only because the variables are binary, but also because of the presence of bilinear products between the binary and other continuous variables. Our proposed convex relaxation to this problem incorporates the bilinear terms in a novel way that improves over the commonly used McCormick approximation. We also address the exponential complexity associated with the discrete variables by partitioning the network graph in a way that minimizes the impact on the optimal value of the relaxation. As a result, the problem is broken down into several parallel mixed-integer problems, reducing the overall computational complexity. Simulations in the IEEE 118-bus test case demonstrate that our approach converges to solutions which are very close to the lower bound of the mixed-integer OPF problem.

For operating electrical power networks, the Optimal Power Flow (OPF) problem plays a central role. The problem is nonconvex and NP hard. Therefore, designing efficient solution algorithms is crucial, though their global optimality is not guaranteed. Existing semi-definite programming relaxation based approaches are restricted to OPF problems for which zero duality holds, whereas for non-convex problems there is a lack of solution methods of provable performance. In this paper, an efficient novel method to address the general nonconvex OPF problem is investigated. The proposed method is based on alternating direction method of multipliers combined with sequential convex approximations. The global OPF problem is decomposed into smaller problems associated to each bus of the network, the solutions of which are coordinated via a light communication protocol. Therefore, the proposed method is highly scalable. The convergence properties of the proposed algorithm are mathematically and numerically substantiated.