Greedy particle swarm and biogeography-based optimization algorithm

The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core.

Optimal online battery power control of grid-connected energy-stored quasi-impedance source inverter with PV system

The fundamental concepts of biogeography-based optimization (BBO), a meta-heuristic algorithm, have been inspired by the geographical distribution of animals. This algorithm does not need a starting point, and performs a random search instead of a gradient-based search. In this article, for the first time, the weights of 2D and 3D trusses with specific geometries and different stress and displacement constraints have been optimized by using the BBO approach. Also, in this work, the numerical results achieved by other researchers through various optimization techniques have been compared with the results obtained from the Particle Swarm Optimization (PSO), Differential Evolution (DE) and BBO algorithms. It has been demonstrated that the search and exploration capability of the BBO algorithm is superior to that of the DE and PSO algorithms, and that it achieves better results than the other optimization techniques considered in this paper. This superiority is due to the excellent exploration capability of the BBO algorithm and the fact that it achieves a favorable optimal solution in the initial iteration.

Biogeography-Based Optimization (BBO) algorithm, a novel optimizing algorithm, is applied in optimal tuning of multimachine Power System Stabilizer (PSS) parameters. The tuning of fixed-structure lead-lag type PSS parameters is formulated as a quadratic performance optimization problem. An eigenvalue-based objective function is chosen to maximize the damping ratio of the electromechanical modes at different system operating conditions. BBO algorithm, robust to the initial solution and computationally efficient compared with other meta-heuristic algorithms, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Evolutionary Strategy (ES), is employed to search for optimal PSS parameters. Simulations are carried out on two typical multimachine power systems. Both frequency-domain eigenvalue analysis and time-domain dynamic simulation verify the effectiveness and robustness of the proposed BBO based PSSs under a wide range operating conditions. Moreover, the superiority of the proposed BBO algorithm is further illustrated by algorithm optimization performance evaluation and dynamic response damping characteristic compared with GA, PSO and ES. Keywords: Biogeography-Based Optimization (BBO), electromechanical oscillations, multimachine power system, parameter optimization, Power System Stabilizer (PSS).

Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974–2013) and partly for testing the models (2014–2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted.• The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan.• The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process.

This paper explores the performance of biogeography-based optimization (BBO) algorithm for solving combined heat and power dispatch (CHPD) or cogeneration problem of power system. BBO is a type of evolutionary algorithm which is based on the theory of biogeography and is inspired by the two concepts, namely migration of species between “islands” via flotsam, wind, flying, swimming, etc. and mutation. To improve the convergence property and solution quality, blended crossover strategy of genetic algorithm (GA) is integrated with conventional BBO algorithm in this study. The effect of valve-point in cost function is considered by adding an absolute sinusoidal term with the conventional polynomial cost function. The potential of the proposed BBO and GA based BBO (GABBO) algorithms are assessed by means of an extensive comparative study of the solutions obtained for small and medium CHPD problems of power systems. To show the priority of the proposed algorithm, comparative studies are carried out to examine the effectiveness of the proposed BBO and GABBO approaches over evolutionary programming (EP), differential evolution (DE), particle swarm optimization (PSO) and time varying acceleration coefficients PSO (TVAC-PSO reported in the literature. The experimental results and comparison with other algorithms demonstrate that the proposed GABBO algorithm can be a proficient substitute lying on solving combined heat and power dispatch problems.

Objectives: Automatic analog circuit design techniques are required to handle the increasing complexity of the circuit with technological advances. To provide better scalability, reliability, and higher convergence speed, the development and assessment of hybrid optimization algorithms are essential. Method: A hybrid Cuckoo Search–Particle Swarm Optimisation (CSPSO) algorithm was implemented for automatically finding of W/L ratios of each transistor in a Folded Cascode OTA (FOTA) circuit. The results of this blended algorithm were evaluated with the results of Particle Swarm Optimisation (PSO) and Differential Evolution (DE) algorithms in 0.18 µm and 0.35 µm CMOS processes. All the algorithms were programmed in the C language, merged with an NGSPICE file of the circuit, and executed on an Intel® Core™ i5 machine running Ubuntu. Findings: The hybrid CSPSO obtained all the desired specifications in 9 out of 10 runs in the 0.35 µm CMOS process for the circuit of FOTA, while PSO and DE algorithms required 8 out of 10 and 5 out of 10 runs to achieve the target specifications. The results demonstrate that the hybrid CSPSO performs better than the other two algorithms in terms of robustness and success rate. In the 0.18µm case, CSPSO and DE succeeded in all runs, whereas PSO showed limited reliability. Novelty: This research work implements a blended CSPSO algorithm for automated sizing of the FOTA circuit and obtains a better convergence speed compared to traditional DE and PSO. These findings establish CSPSO as a highly effective and scalable optimization paradigm for automated analog circuit design across technology nodes. Keywords: EA, DE algorithm, PSO algorithm, CSPSO algorithm

The integration of distributed generation (DG) resources is growing day by day in electricity grids. There are many reasons which persuade the operators to utilize DGs such as restrictions on the construction and development of transmission lines and distribution network, transition of traditional power systems to restructured electricity markets, the competitive conditions in wholesale and retail markets, the implication of economic and environmental issues in the production of electrical energy, increasing the system reliability and customer satisfactory level. The reliability improvement capability of power systems by utilization of these units has attracted the attention of many electrical engineering experts and power system planners and operators. Reliability plays a prominent role in the satisfaction of all power industry participants, especially the consumers of the electricity. In this paper, the effect of distributed generation units on the power system reliability has been investigated. Therefore, the virus colony search (VCS) algorithm is employed to determine the optimal placement and size of distributed generators subject to improve the reliability indices. The simulation is carried out on a 34-bus IEEE test network. The optimization results are also compared with the results of both genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolution (DE) algorithm, multi-objective particle swarm optimization (MOPSO) algorithm, modified shuffled frog leaping algorithm (MSFLA), gravitational search algorithm (GSA), biogeography-based optimization (BBO) algorithm, hybrid big bang-big crunch (HBB-BC) algorithm and glowworm swarm optimization (GSO) algorithm.

A hybrid biogeography-based optimization algorithm for job shop scheduling problem

A self-tuning method to determine the appropriate parameters of a proportional-integral-derivative controller for an automatic voltage regulator (A VR) system using a biogeography-bas ed optimization (BBO) algorithm is proposed in this study. The BBO algorithm was developed based on the theory of biogeography, which describes migration and its results. We propose that the BBO algorithm has a high-quality solution and stable convergence characteristics, and thus it improves the transient response of the controlled system. The performance of the BBO algorithm depends on the transient response, root locus, and Bode analysis. Robustness analysis is done in the A VR system, which is tuned by an articial bee colony (ABC) algorithm in order to identify its response to changes in the system parameters. We compare the BBO algorithm with the ABC algorithm, particle swarm optimization algorithm, and differential evolution algorithm. The results of this comparison show that the BBO algorithm has a better tuning capability than the other optimization algorithms.

Evolutionary computing algorithms play a great role in solving real time optimization problems. One of the evolutionary computing algorithm is Particle Swarm Optimization algorithm (PSO). The aim of this paper is to propose a model to improve the performance of PSO algorithm. Hybrid models of Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) has already proved to be one of the better approaches for solving real world complex, dynamic and multimodal optimization problems. But these models hybridize PSO and DE to form a new serial algorithm. In these serial hybridization models, we are losing the originality of both DE and PSO algorithms since the structure of both the algorithms is being modified to get the hybridized PSO and DE algorithm. In this paper, we develop a model for PSO in distributed environment with improved performance in terms of speed and accuracy. The proposed model is a hybridized distributed mixing of DE and PSO (dm-DEPSO) which improves the performance of PSO algorithm. In this model, algorithms are implemented in a cluster environment to perform co-operative co-evolution. Better solutions are migrated from one node to another in the cluster environment. Co-operative co-evolving model shows better performance in terms of speed and accuracy. The algorithm is applied to a set of eight benchmarking functions and their performance are compared by mean of objective function values, standard deviation of objective function values, success rate, probability of convergence and quality measure.

We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algorithm is tested and compared with the standard PSO and DE methods on 14 benchmark problems to illustrate that DPSE has the potential to achieve a faster convergence and a better solution. Simulation results show that, on the average, DPSE outperforms DE by 39.20% and PSO by 14.92% on the 14 benchmark problems. To show the feasibility of the proposed strategy on a real-world optimization problem, an application of DPSE to optimize the parameters of active disturbance rejection control (ADRC) in PUMA-560 robot is presented. I. INTRODUCTION The particle swarm optimization (PSO) algorithm was originally introduced in (1) as an alternative to the standard genetic algorithm (GA). The PSO was inspired by insect swarms and has since proven to be a competitor to the standard GA when it comes to function optimization. Since then several researchers have analyzed the PSO performance and disadvantages (2-4) and their research indicates that it performs well in the early iterations but has problems reaching a near optimal solution in several real-valued function optimization problems. Differential evolution (DE) is a simple yet powerful evolutionary algorithm (EA) for global optimization introduced by Price and Storn (5). Both PSO and DE received great interest from the evolutionary computation community, and showed great promise in several real-world applications (6-9). As a result, a lot of effort has been spent recently in combining both methods to achieve better optimization result. One method proposed in 2003 (10) applies to the modeling of gene regulator networks in 2007 (11). In this algorithm, the mutations provided by DE operator are applied, and only applied on the personal best individual to prevent the swarm from disorganizing by unexpected fluctuations. Later in 2008, (12) also presents a method of hybrid PSO with DE and its application to a high-frequency transformer can be seen in (13). In this algorithm, the DE mutations are applied to update both personal best and global best. At the same time, Swagatam Das et al. (14) proposed a hybridization of PSO and DE for continuous optimization in 2008. Based on that idea, (15) apply this algorithm for the black-box optimization benchmarking for noisy functions.

Biogeography-based Optimization (BBO) Algorithm for Single Machine Total Weighted Tardiness Problem (SMTWTP)

Economic optimization design for shell-and-tube heat exchangers by a Tsallis differential evolution

Task scheduling, which plays a crucial role in cloud computing and is the critical factor influencing the performance of cloud computing, is an NP-hard problem that can be solved with a heuristic algorithm. In this paper, we propose a novel heuristic algorithm, called biogeography-based optimization (BBO), and a new hybrid migrating BBO (HMBBO) algorithm, which integrates the migration strategy with particle swarm optimization (PSO). Both methods are proposed to solve the problem of scheduling-directed acyclic graph tasks in a cloud computing environment. The basic idea of our approach is to exploit the advantages of the PSO and BBO algorithms while avoiding their drawbacks. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. In HMBBO, the flight strategy under the BBO migration structure is hybridized to accelerate the search speed, and HEFT_D is used to evaluate the task sequence. Based on the WorkflowSim, a comparative experiment is conducted with the makespan of task scheduling as the objective function. Both simulation and real-life experiments are conducted to verify the effectiveness of HMBBO. The experiment shows that compared with several classic heuristic algorithms, HMBBO has advantages in terms of global search ability, fast convergence rate and a high-quality solution, and it provides a new method for task scheduling in cloud computing.