A Glowworm Swarm Optimization Algorithm for the Multi-Objective Energy Reduction Multi-Depot Vehicle Routing Problem

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.

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.

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

Battle Royale Optimizer (BRO) is a recently proposed optimization algorithm that has added a new category named game-based optimization algorithms to the existing categorization of optimization algorithms. Both continuous and binary versions of this algorithm have already been proposed. Generally, optimization problems can be divided into single-objective and multi-objective problems. Although BRO has successfully solved single-objective optimization problems, no multi-objective version has been proposed for it yet. This gap motivated us to design and implement the multi-objective version of BRO (MOBRO). Although there are some multi-objective optimization algorithms in the literature, according to the no-free-lunch theorem, no optimization algorithm can efficiently solve all optimization problems. We applied the proposed algorithm to four benchmark datasets: CEC 2009, CEC 2018, ZDT, and DTLZ. We measured the performance of MOBRO based on three aspects: convergence, spread, and distribution, using three performance criteria: inverted generational distance, maximum spread, and spacing. We also compared its obtained results with those of three state-of-the-art optimization algorithms: the multi-objective Gray Wolf optimization algorithm (MOGWO), the multi-objective particle swarm optimization algorithm (MOPSO), the multi-objective artificial vulture's optimization algorithm (MOAVAO), the optimization algorithm for multi-objective problems (MAOA), and the multi-objective non-dominated sorting genetic algorithm III (NSGA-III). The obtained results approve that MOBRO outperforms the existing optimization algorithms in most of the benchmark suites and operates competitively with them in the others.

2-Chlorophenol is a kind of representative organic waste water. With the environmental pollution becoming increasingly serious, and the large amount of waste discharged and the increasing difficulty of treatment, the research on the kinetics of the oxidation of supercritical water of 2-chlorophenol has important significant. Aiming at the phenomenon that the Glowworm Swarm Optimization (GSO) algorithm has slow convergence, low precision and easy to get trapped into local optimum, this paper presents an improved version of the GSO based on the behavior of predator-prey and biological predator, and we call it dual population Glowworm Swarm Optimization (GSOPP). The algorithm accelerates the convergence speed by introducing strategies such as chase and escape and variation among populations, and can obtain a more accurate solution. Tested by three standard test functions, the results showed that the improved GSOPP algorithm had better performance than the basic GSO algorithm. Finally, the algorithm was applied to estimate the parameter estimation of the supercritical water oxidation kinetics of 2-chlorophenol, and satisfactory results were obtained.

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.

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%.

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.

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.

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 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.

This paper introduces a caching enabled parallel multi-objective tri-level particle swarm optimization algorithm (MO-3LPPSO) with objective to address a challenging NP-hard problem from bioinformatics i.e. structural alignment of complex RNA sequences. MO-3LPPSO implements master-slave topology based communication strategy on the parallely connected machines via message passing interface (MPI). Level 1 of the proposed algorithm acquires the optimal alignment of the sequences distributed on slave processors in the order of their complexities. Further, the aligned sequences along with their alignment scores are stored on the master processor. In the second level, the secondary structures of all the gbest aligned sequences of level 1 is obtained. Each sequence set is distributed on a slave processors, that constructs secondary structure of all sequences from the set. The alignment scores and secondary structure scores obtained from level 1 and level 2, now move towards level 3, forming a bi-objective optimization problem with the objectives to maximize sequence similarity score and minimize free energy score for most stable RNA secondary structure. The top-level non-dominated solutions are extracted further in level three and the external archive in Ctrie is updated. The improvement from MO-TLPSO to MO-3LPPSO has been remarkable in the sense: inclusion of Ctrie enables the algorithm to work with multi-client environment for handling RNA structural alignment queries; implementation of parallelization facilitates structural alignment of highly complex massive datasets of RNA sequences. Further, the difference between the time taken by MO-TLPSO and MO-3LPPSO is found extremely significant, as confirmed by non-parametric statistical test Mann-Whitney U test. Further, the structural alignment of highly complex sequence sets is performed by MO-3LPPSO, which is tested for prediction accuracy and processing time. The algorithm is found producing highly accurate results at significantly lesser processing time.