Swarm Optimization Strategy Research Articles

CAPSO: Centripetal accelerated particle swarm optimization

Meta-heuristic search algorithms are developed to solve optimization problems. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. Swarm intelligence (SI) algorithms comprise a branch of meta-heuristic algorithms that imitate the behavior of insects, birds, fishes, and other natural phenomena to find solutions for complex optimization problems. In this study, an improved particle swarm optimization (PSO) scheme combined with Newton’s laws of motion, the centripetal accelerated particle swarm optimization (CAPSO) scheme, is introduced. CAPSO accelerates the learning and convergence of optimization problems. In addition, the binary mode of the proposed algorithm, binary centripetal accelerated particle swarm optimization (BCAPSO), is introduced for binary search spaces. These algorithms are evaluated using nonlinear benchmark functions, and the results are compared with the gravitational search algorithm (GSA) and PSO in both the real and the binary search spaces. Moreover, the performance of CAPSO in solving the functions is compared with some well-known PSO algorithms in the literature. The experimental results showed that the proposed methods enhance the performance of PSO in terms of convergence speed, solution accuracy and global optimality.

Influence of Optimization Algorithm on Airfoil Shape Optimization of Aircraft Wings

Computational fluid dynamics (CFD) is one of the computer-based solution methods which are more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increases as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. The Method of search algorithms or optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays various optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc., are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimization process. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, Hicks- Henne Bump function, PARSEC etc. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Particle Swarm optimization Algorithm which is one kind of Non-Traditional Optimization technique to optimize the aerodynamic characteristics of a general airfoil for specific conditions. An aerodynamic shape optimization problem is formulated for NACA 2411 airfoil and solved using the method of Particle Swarm Optimization for 5.0 deg angle of attack. A MATLAB program has been developed to implement PARSEC, Panel Technique, and PSO Algorithm. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries has been calculated using panel method. NACA 2411 airfoil has been generated using PARSEC and optimized for 5.0 deg angle of attack using PSO Algorithm. The results show that the particle swarm optimization scheme is more effective in finding the optimum solution among the various possible solutions.

Antenna combining and power allocation-assisted multiple-input multiple-output cooperative communications: a joint approach

In this study, the authors present a particle swarm optimisation (PSO) scheme for the joint issue of antenna combing (AC) and power allocation in order to concurrently enhance throughput and reduce hardware cost for multiple-input multiple-output cooperative systems. In the proposed scheme, each particle's position is characterised by three matrices: the receive AC matrix of the cooperative relay; the receive AC matrix of the destination node; and the power allocation matrix of the cooperative relay. Based on the constrained maximisation of mutual information, the proposed scheme carries out the PSO's iterative update mechanisms to concurrently refine the position of each particle until the largest mutual information is attained. Simulation results show that the proposed scheme is guaranteed to converge, outperforms existing schemes of antenna selection or power allocation at the price of higher computational load and achieves near-optimal capacity of the benchmark (full radio frequency (RF) chains) scheme at a significant saving of RF chains.

Influence of Search Algorithms on Aerodynamic Design Optimisation of Aircraft Wings

The Method of search algorithms or optimisation algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimisation problem. Nowadays various optimisation methods such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimisation (PSO) etc., are more widely employed to solve the aerodynamic shape optimisation problems. In addition to the optimisation method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimisation process. Since the reduction in the number of design parameters is one of the most important requirements for the aerodynamic shape optimisation problem, it becomes important to mathematically describe the airfoil geometry with minimum number of design parameters. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimisation algorithms. It is also demonstrated that the estimation of a suitable optimisation scheme for a given optimisation problem. An aerodynamic shape optimisation problem is formulated for NACA 0012 airfoil and solved using the methods of Particle Swarm Optimisation and Genetic Algorithm for 5.0 deg angle of attack. The results show that the particle swarm optimisation scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the PSO shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.

Employing Adaptive Particle Swarm Optimization Algorithm for Parameter Estimation of an Exciter Machine

Winding inductances of an exciter machine of brushless generator normally consist of nonsinusoidal terms versus rotor position angle, so evaluations of the inductances necessitate detailed modeling and complicated parameter identification procedures. In this paper, an adaptive particle swarm optimization (APSO), which is a novel heuristic computation technique, is proposed to identify parameters of an exciter machine. The proposed approach evaluates the model parameters just knowing the main field impedance, measured exciter field voltage and current. APSO is employed to solve the optimization problem of minimizing the difference between output quantities (exciter field current) of the model and real systems. Two modifications are incorporated into the conventional particle swarm optimization (PSO) scheme that prevents local convergence and provides excellent quality of final result. Performance of the proposed APSO is compared with those of the real-coded genetic algorithm (GA) and PSO with linearly decreasing inertia weight (LDW-PSO), in terms of the parameter accuracy and convergence speed. Simulation results illustrated in the paper show that the proposed APSO is more successful in comparison with LDW-PSO and GA.

ADRC and CMAC combined optimization and control for a class of discrete-time uncertain chaotic systems

Immune dynamic particle swarm optimization (IDPSO) strategy integrated with active disturbance rejection control (ADRC) and cerebellar model articulation controller (CMAC) combined control is designed for uncertain nonlinear discrete-time chaotic systems. The ADRC-CMAC is comprised of a cerebellar model articulation controller (CMAC) and an ADRC controller. The ADRC controller is designed to guarantee the stability of the system and restrict the disturbance. The CMAC is used to guarantee the control precision and response speed. Immune binary-state particle swarm algorithm is used to tune online the parameters of the ADRC-CMAC. Simulation results of uncertain nonlinear discrete-time systems demonstrate that performance with favorable response speed and restrained disturbance can be achieved by using the proposed control system.

Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB

This investigation proposes an improved particle swam optimization (PSO) approach to solve the resource-constrained scheduling problem. Two proposed rules named delay local search rule and bidirectional scheduling rule for PSO to solve scheduling problem are proposed and evaluated. These two suggested rules applied in proposed PSO facilitate finding global minimum (minimum makespan). The delay local search enables some activities delayed and altering the decided start processing time, and being capable of escaping from local minimum. The bidirectional scheduling rule which combines forward and backward scheduling to expand the searching area in the solution space for obtaining potential optimal solution. Moreover, to speed up the production of feasible solution, a critical path is adopted in this study. The critical path method is used to generate heuristic value in scheduling process. The simulation results reveal that the proposed approach in this investigation is novel and efficient for resource-constrained class scheduling problem.

Dynamic computerized testlet-based test generation system by discrete PSO with partial course ontology

For the majority of E-learning systems their important role is the electronic teachers who can assist students to learn and review courses enrolled. Unfortunately, an E-learning system does not resemble a teacher who can evaluate the current students’ learning status by the immediate class observations and adjust his/her policy accordingly. Hence, to be able to dynamically compose tests to examine students’ learning status and clear their confusions becomes an important issue. This paper argues that how to formalize test requirements dynamically and how to select suitable items effectively are two main challenges in the current researches of computerized test generation. Hence, this article proposed a novel approach that can compose suitable tests to easily and efficiently evaluate the learning statuses of students. The proposed approach obeys the Discrete Particle Swarm Optimization (DPSO) scheme and uses partial course ontology to generate tests according to the studied materials. Besides, it also harnesses the notion of testlets to construct testlet-based tests for students. In the experiments, the results show that the proposed approach can effectively formalize the tested concepts and their weights, and also generate the well-scrutinized test in accordance with the test requirements in the acceptable time.

Pareto optimal 2D joint inversion of gravity and magnetic data

Building models of a geological system using potential field is almost a routine exercise, where both gravity and magnetic data are used to interpret the architectural framework of a geological setting. Although nonuniqueness in potential field interpretation is inevitably a major issue (Skeels, 1947, Roy, 1962), it is argued by many authors (Pedersen, 1979; Bosch et al., 2006) that the use of data redundancy, for example, joint use of gravity and magnetic data as well as appropriate constraints in the interpretation/inverse modelling overcomes the ambiguity limitation of potential field interpretation. However, joint use of gravity and magnetic data especially for geometric inversion is a non-trivial task. By geometric inversion of potential field data we mean determining the geometric configuration of the modelled body whose physical properties, such as density/susceptibility are known. Such potential field inversions are inherently nonlinear. The presence of nonlinearity between the data and model space often results in an apparent paradoxical situation, offering a less stable and thus a less reliable inverse model while exercising the data redundancy by joint use of gravity and magnetic data. We address the issue of producing a stable solution for a 2D joint geometric inversion of gravity and magnetic data via the ?Pareto optimal? criterion. To this end, we state that the joint inversion of gravity and magnetic data can be realised via a multi-objective optimisation problem (MOP), where the objective functions related to the best fitting gravity and magnetic data are satisfied simultaneously. Very recently, MOP has been introduced by Kozlovskaya et al. (2007) in seismic anisotropy studies of lithosphere by jointly inverting shear wave splitting and P-wave residual data. With regard to the MOP our goal is not to determine a unique, possibly globally optimal, solution, but to determine a class of optimal solutions defining a compact set. Such a class of solutions is known as a Pareto set or Pareto front, where each solution is optimal in a Pareto sense; meaning there exists no other solution which will improve one objective function without degrading the other objective function (Coello Coello et al., 2002). To determine a Pareto front we use the epsilon-constraint method, where one objective is minimised while the other one is constrained with a bound by a pre-selected value of epsilon. To minimise the objective function we use a Particle Swarm Optimization (PSO) scheme, which is a global stochastic optimisation scheme. The rationale in selecting PSO is that it is easy to implement and is computationally inexpensive since its memory and its CPU speed requirements are low (Jones, 2006). PSO has been used for both continuous and discrete optimisation problems (Clerc and Kennedy, 2002). The application of PSO in geophysical inversion is demonstrated by Shaw and Shrivastava, (2007). In this study we discuss the application of MOP and PSO in the joint inversion of gravity/magnetic data as well as testing the method through numerical experiment using synthetically generated gravity and magnetic data for a simple but realistic geological model.

Particle swarms applied to array synthesis and planar near‐field antenna measurements

AbstractThe usefulness and limitations of several particle swarm optimization schemes when applied to linear array synthesis considering directive element patterns and to antenna far‐field pattern reconstruction from planar near‐field acquisition are presented. Particle swarm optimization with either synchronous or asynchronous updates of the swarm as well as global or local topologies have been considered. For both applications, results comparing the four optimization techniques are reported and discussed. Finally, results considering complex and phase‐only linear array synthesis, along with results of near‐field to far‐field transformation for a pyramidal horn are also included. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 50: 544–548, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.23089

Particle swarm optimization for pap-smear diagnosis

The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper, a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in different times by expert Medical Doctors, consisting of 917 and 500 images of pap-smear cells, respectively. Each cell is described by 20 numerical features and the cells fall into seven classes but a minimal requirement is to separate normal from abnormal cells which is a two-class problem. For finding the best possible performing feature subset, an effective particle swarm optimization scheme is proposed. This algorithmic scheme is combined with a number of nearest neighbor based classifiers. Results show that classification accuracy generally outperforms other previously applied intelligent approaches.

Memetic particle swarm optimization

We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are compared to that of the global and local variants of Particle Swarm Optimization, justifying the superiority of the memetic approach.

A Novel Approach for Unit Commitment Problem via an Effective Hybrid Particle Swarm Optimization

This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods.

Application of a parallel particle swarm optimization scheme to the design of electromagnetic absorbers

The application of parallel (synchronous) particle swarm optimization (PSO) to electromagnetic absorber design is described. Synchronous PSO is a version of PSO that allows for parallelization and hence faster optimization. The velocity updating rule is empirically determined, and a simple approach is presented to improve its robustness. Absorbers designed with the PSO approach are compared with absorbers designed by genetic algorithms (GAs). Numerical results demonstrate that the PSO is able to deliver absorbers with comparable performance to GA-designed absorbers, with much less computational cost.