Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems

A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring

An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results

On a novel hybrid Manta ray foraging optimizer and its application on parameters estimation of lithium-ion battery

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon's test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.Web of Science151art. no. 6

Quantum behaved artificial bee colony based conventional controller for optimum dispatch

Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybrid- particle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

A New Fusion of Salp Swarm with Sine Cosine for Optimization of Non-linear Functions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the Salp Swarm Algorithm with Sine Cosine Algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated by using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in Salp Swarm Optimizer Algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on twenty-two standard mathematical optimization functions and three applications namely the three-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others

Particle swarm optimization using dimension selection methods

a b s t r a c t Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the PSO, and then gives a PSO variant without randomness to show that traditional PSO cannot work without randomness. Based on our analysis of the randomness, another way of using randomness is proposed in PSO with random dimension selection (PSORDS) algorithm, which utilizes random dimension selection instead of stochastic coefficients. Finally, deterministic methods to do the dimension selection are proposed, and the resultant PSO with distance based dimension selection (PSODDS) algorithm is greatly superior to the traditional PSO and PSO with heuristic dimension selection (PSOHDS) algorithm is comparable to traditional PSO algorithm. In addition, using our dimension selection method to a newly proposed modified particle swarm optimization (MPSO) algorithm also gets improved results. The experiment results demonstrate that our analysis about the randomness is correct and the usage of deterministic dimension selection method is very helpful

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach