Predator-Prey Behavior Firefly Algorithm for Solving 2-Chlorophenol Reaction Kinetics Equation

Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.

The glowworm swarm optimisation (GSO) algorithm is a novel algorithm with the simultaneous computation of multiple optima of multimodal functions. Data-clustering techniques are classification algorithms that have a wide range of applications. Since K-means algorithm is easy to fall into the local optimum by the selection of initial clustering centre, GSO algorithm is used for the telecom customers clustering. The customer consumption data is extracted by means of the recency frequency monetary (RFM) model and the standardised data is clustered automatically using the GSO algorithm's synchronous optimisation ability. In the clustering optimisation algorithm, adaptive step size is used instead of the original fixed step size to avoid local optimisation of the algorithm and obtain higher accuracy. Compared with K-means clustering algorithm, GSO approach can automatically generate the number of clusters and use RFM model to reduce effectively the size of the data processing. The results of the experiments demonstrate that the GSO-based clustering technique is a promising technique for the data clustering problems.

This study proposes glowworm swarm optimization (GSO) algorithm to estimate an improved value of machining performance measurement. GSO is a recent nature-inspired optimization algorithm that simulates the behavior of the lighting worms. To the best our knowledge, GSO algorithm has not yet been used for optimization practice particularly in machining process. Three cutting parameters of end milling that influence the machining performance measurement, minimum surface roughness, are cutting speed, feed rate and depth of cut. Taguchi method is performed for experimental design. The analysis of variance is applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. GSO has improved machining process by estimating a much lower value of minimum surface roughness compared to the results of experimental and particle swarm optimization.

In order to keep the flux constant when the asynchronous motor is running under load, especially under light load, a Glowworm swarm optimization (GSO) algorithm is proposed to control the voltage frequency ratio of the cycloconverter. Based on the analysis of the two variable control theory of the voltage frequency ratio of the cycloconverter and the working principle of the cycloconverter, the self-control method of the voltage frequency ratio of the cycloconverter by adaptive fuzzy backstepping sliding mode is proposed. In the control process, GSO and BFA are integrated to optimize the control process of the controller, and an adaptive selection strategy is adopted to ensure the adaptive step size of the firefly and improve the accuracy of the GSO algorithm. The experimental results show that when the optimal voltage-frequency ratio is switched between the online continuous frequency control strategy and the frequency range is small enough, the motor speed is stable, the speed regulation process is stable, and the motor is always in the state of high force index. It shows that the control method has a fast convergence speed and a good control effect on the voltage frequency ratio of the ring converter of the experimental motor, and it can keep the flux constant under light load.

Glowworm Swarm Optimization Algorithm (GSO) is one of new swarm intelligence optimization algorithms in recent years. Its main idea comes from the cooperative behavior source among individuals during the process of courtship and foraging. In this paper, in order to improve convergence speed in the late iteration, avoid the algorithm falling into local optimum, and reduce isolated nodes, the Adaptive Step Mechanism Glowworm Swarm Optimization (ASMGSO) is proposed. The main idea of ASMGSO algorithm is as follows: (1) On the basis of SMGSO algorithm, isolated nodes carry out bunching operator first, that is to say they are moving to the central position of the group. If the new position is not better than the current position, then isolated nodes perform mutation operation. (2) At the same time, the fixed step mechanism has been improved. Each individual has its step size which is adapted to itself in the iterative process. The step size is not only affected by the node's neighborhood density but also affected by the distance to the moving target node. The effectiveness of the proposed ASMGSO algorithm is verified through several classic test functions and application in Distance Vector-HOP.

The aim of this study is to develop an integrated study of surface roughness (Ra) in the die-sinking electrical discharge machining (EDM) process of Ti-6AL-4V titanium alloy with positive polarity of copper-tungsten (Cu-W) electrode. Regression analysis and glowworm swarm optimization (GSO) algorithm were considered for modelling and optimization process. Pulse on time (A), pulse off time (B), peak current (C) and servo voltage (D) were selected as the machining parameters with various levels. The experiments have been conducted based on the two levels of full factorial design with an added center point design of experiments (DOE). Moreover, mathematical models with linear and 2 factor interaction (2FI) effects of the parameters chosen were developed. The validity test of the fit and the adequacy of the developed mathematical models have been carried out by using analysis of variance (ANOVA) and F-test. The statistical analysis showed that the 2FI model outperformed with the most minimal value of Ra compared to the linear model and experimental result.

In this chapter, we present, initially, a literature review of the most recent publications for the glowworm swarm optimization (GSO) algorithm. Then, we propose a modified version of the algorithm, the parallel multi-start multiobjective glowworm swarm optimization (PMS-GSO) algorithm, which is suitable for multi-objective routing problems. The proposed PMS-GSO was used for the solution of a very complex vehicle routing problem, the multi-objective route-based energy reduction multi-depot vehicle routing problem (MERMDVRP). We solved four different versions of the problem, from the simplest one, where we had a problem without real-life constraints, to the most difficult one, where a number of real-life constraints based mainly on the behavior of the driver, the road, and the weather conditions have been added. Finally, we compared the computational results of the proposed algorithm with the computational results of three other algorithms, the parallel multi-start non-dominated sorting genetic algorithm II (PMS-NSGA-II), the parallel multi-start multi-objective firefly (PMS-FIREFLY) algorithm, and the parallel multi-start multi-objective particle swarm optimization (PMS-NSPSO) algorithm in a number of benchmark instances.

Electric Discharge Machining (EDM) is a non-traditional machining process that utilizes electric sparks between an electrode and a workpiece submerged in a dielectric fluid to ablate a material. It is commonly used in die-making, aerospace, automotive manufacturing, and medical manufacturing, because it can machine hard and complex materials with high precision. In this work, a Surface Roughness Optimization for EDM (SRO-EDM) model is proposed to investigate the machining performance of the die-sinking EDM process of titanium alloys. A regression-based combined approach of Glowworm Swarm Optimization (GSO) and a Two-Factor Interaction (2FI) model has been proposed to investigate the impact of four key process variables, namely voltage (V), peak current (Ip), pulse-on time (ton), and pulse-off time (toff) on surface roughness (Ra) at various locations on work surfaces. A Central Composite Design (CCD) was applied to systematically investigate parameter combinations. Statistical analysis was performed using analysis of variance (ANOVA), which confirmed the statistical significance of the selected parameters, and 2FI regression (R² = 0.60) with moderate-fit predictive accuracy was established. To enhance the quality of optimization, the Enhanced Glowworm Swarm Optimization (EGSO) algorithm is proposed by hybridizing the GSO with Artificial Fish Swarm (AFS) algorithm. The AFS module improves the exploration capability of the GSO and alleviates the local optima problem. For the experimental validation of the model, Response Surface Methodology (RSM) was used to generate the regression based on the developed model and as an objective function for optimization. Experimental results show that EGSO outperforms GSO in performance to achieve an optimized Ra ( ) compared to through conventional GSO. The results demonstrate that the EGSO model can improve convergence accuracy and speed and is a practical method for EDM surface quality optimization in the high-precision manufacturing industry.

When the basic glowworm swarm optimization (GSO) algorithm optimizes the multi-peak function, the solution accuracy is not high, the later convergence is slow. To solve these problems, the fluorescent factor is introduced to adaptively adjust the step length of the firefly, an adaptive step length firefly optimization algorithm is proposed, this algorithm is an improved self-adaptive step glowworm swarm optimization (ASGSO). In this algorithm, the behavior of glowworms are developed, the step size is dynamically adjusted by the fluorescent factor, the algorithm avoids falling into a local optimum and improves the optimization speed and accuracy. The simulation results show that the improved ASGSO can search for global optimization more quickly and precisely.

In this paper, two new cluster analysis methods based on glowworm swarm optimization (GSO) algorithm are proposed. The first algorithm showed how GSO can be used to self-organization cluster analysis. The second algorithm is hybrid the GSO clustering analysis with the K-means algorithm to accelerate classification. Two clustering algorithms are tested on three data sets; experimental results show that the two kind of clustering algorithm has higher clustering results.

In communication networks, the multi-constraint multicast communication is an important way to improve the efficiency of network operation and quality of service. Some heuristic algorithms are applied in solving multicast routing problem under multiple constraints, such as simulated annealing, genetic algorithm, ant colony algorithm and particle swarm optimization algorithm. However, these algorithms suffer from low convergence rate and high computational complexity in solving multi-constraint multicast routing problems. The glowworm swarm optimization (GSO) algorithm is a promising algorithm recently arisen, which can overcome such shortcomings. This paper proposes a GSO based multi-constraint multicast (GSO-MCM) algorithm, which can efficiently generate multicast routing tree to meet the multi-constraint requirements. The simulation result manifests that the GSO-MCM algorithm proposed in this paper performs well in searching, converging speed and adaptability scale.

Breast imaging technique called mammography has gained bigger attention among the researchers for the diagnosis of breast malignancy in the woman. Mammogram screening is the most effective procedure to visualize various potential problems in the breast. The two most common features connected with breast tumors are mass lesions and microcalcification. The collection of suitable image preprocessing, segmentation, feature extraction, selection and prediction algorithms play an essential role in the accurate detection and classification of cancer on mammograms. Classification techniques estimate unlabeled datasets class labeling depending on its similarity to the pattern learned. The Glowworm Swarm Optimization(GSO) algorithm is ideal for finding several solutions, and dissimilar or equivalent objective function values at the same time. This feature of GSO is useful for optimizing the feature set obtained from multiscale feature extraction procedures. Poor performance in generalization is the issue that arises due to the unconditioned output matrix of the hidden stage of the ELM classifier. The optimization algorithms will address this matter because of their global search capabilities. This article suggests ELM with the Fruitfly Optimization Algorithm (ELM‐FOA) along with GSO to regulate the input weight to achieve maximal performance at the hidden node of the ELM. The testing precision and sensitivity of GSO‐ELM‐FOA are 100% and 97.91%, respectively. The system developed will detect the calcifications and tumors with an accuracy of 99.15%.

Aiming at the problem of host load forecasting in mobile cloud computing, the Long Short Term Memory networks (LSTM) is introduced, which is suitable for the complex and long-time series data of the cloud environment and a load forecasting algorithm based on Glowworm Swarm Optimization LSTM neural network is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are superior to similar prediction algorithms in prediction accuracy.

Glowworm Swarm Optimization (GSO) is one of the common swarm intelligence algorithms, where GSO has the ability to optimize multimodal functions efficiently. In this paper, a parallel MapReduce-based GSO algorithm is proposed to speedup the GSO optimization process. The authors argue that GSO can be formulated based on the MapReduce parallel programming model quite naturally. In addition, they use higher dimensional multimodal benchmark functions for evaluating the proposed algorithm. The experimental results show that the proposed algorithm is appropriate for optimizing difficult multimodal functions with higher dimensions and achieving high peak capture rates. Furthermore, a scalability analysis shows that the proposed algorithm scales very well with increasing swarm sizes. In addition, an overhead of the Hadoop infrastructure is investigated to find if there is any relationship between the overhead, the swarm size, and number of nodes used.

With the rapid development of indoor positioning technology, radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-line-of-sight, non-contact and rapid identification. However, the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency, and their environmental robustness is not strong enough. In this paper, we have introduced an RFID positioning algorithm based on the Glowworm Swarm Optimization (GSO) fused with semi-supervised online sequential extreme learning machine (SOS-ELM), which is called the GSOS-ELM algorithm. The GSOS-ELM algorithm automatically adjusts the regularization weights of the SOS-ELM algorithm through the GSO algorithm, so that it can quickly obtain the optimal regularization weights under different initial conditions; at the same time, the semi-supervised characteristics of the GSOS-ELM algorithm can significantly reduce the number of labeled reference tags and reduce the cost of positioning systems. In addition, the online learning phase of the GSOS-ELM algorithm can continuously update the system to perceive changes in the environment and resist the environmental interference. We have carried out experiments to study the influence factors and validate the performance, both the simulation and testbed experiment results show that compared with other algorithms, our proposed GSOS-ELM localization system can achieve more accurate positioning results and has certain adaptability to the changes of the environment.