Multi-objective Firefly Algorithm Research Articles

Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

Movement Mode Harmony Search Based Multi-objective Firefly Algorithm Feature Selection for Detecting the Security Threats in Virtual Machine

A large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization

A large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization

Multi‐objective Firefly algorithm combining logistic mapping and Cauchy mutation

SummaryIn the process of evolution, the multi‐target Firefly algorithm has a single learning strategy and is easy to fall into premature convergence, resulting in poor comprehensive performance of the algorithm. To solve this problem, a multi‐objective firefly algorithm (MOFA‐LM) combining logistic mapping and Cauchy mutation is proposed in this article. To improve the uneven distribution of the initial population, the initial population with good ergodicity is generated by logistic mapping. Aiming at the problem that the population is easy to fall into local optimal, Cauchy mutation can generate relatively large disturbance when fireflies are updating their positions, which makes it easier to jump out of the local optimal value with a wide range of individual optimization, effectively improve the optimization accuracy of the algorithm, overcome the premature convergence of the algorithm and maintain the convergence of the population. In the experimental part, the proposed algorithm is compared with some multi‐objective optimization algorithms in 19 benchmark test functions, and the effectiveness of the strategy added by MOFA‐LM is verified. The results show that MOFA‐LM has obvious advantages over other algorithms in improving population convergence and distribution.

Multi-objective firefly algorithm with adaptive region division

Aiming at the problems of single optimization strategy and poor comprehensive performance of MOFA, multi-objective firefly algorithm with adaptive region division is proposed in this paper. By leveraging the convergence index, our algorithm intelligently divides the dominant and non-dominant solution groups into three sub-regions, namely balance, exploration, and development areas, each with a distinct learning strategy that complements the strengths of fireflies. Specifically, fireflies in the balance area learn from global optimal particles with diversity to achieve a balanced exploration and development ability. Fireflies in the exploration area jointly learn from globally optimal particles with convergence and diversity, increasing the algorithm’s likelihood of discovering Pareto optimal solutions. Lastly, fireflies in the development area rapidly converge under the guidance of the globally optimal particle of convergence, thus improving the algorithm’s development ability. To further enhance the comprehensive optimization performance, we introduce a novel fusion index as an external archive update strategy that preserves solutions with superior convergence and diversity. Our experiments on 20 benchmark functions and a multi-objective optimization power flow example demonstrate that our algorithm outperforms other multi-objective optimization algorithms, highlighting its superior optimization performance.

Ride comfort and segmental vibration transmissibility analysis of an automobile passenger model under whole body vibration

The examination of seated occupants’ ride comfort under whole-body vibration is a complex topic that involves multiple factors. Whole-body vibration refers to the mechanical vibration that is transmitted to the entire body through a supporting surface, such as a vehicle seat, when traveling on rough or uneven surfaces. There are several methods to assess ride comfort under whole-body vibration, such as subjective assessments, objective measurements, and mathematical models. Subjective assessments involve asking participants to rate their perceived level of discomfort or satisfaction during the vibration exposure, typically using a numerical scale or questionnaire. Objective measurements include accelerometers or vibration meters that record the actual physical vibrations transmitted to the body during the exposure. Mathematical models use various physiological and biomechanical parameters to predict the level of discomfort based on the vibration data. The examination of seated occupants ride comfort under whole-body vibration has been of great interest for many years. In this paper, a multi-body biomechanical model of a seated occupant with a backrest is proposed to perform ride comfort analysis. The novelty of the present model is that it represents complete passenger by including thighs, legs, and foot which were neglected in the past research. A multi-objective firefly algorithm is developed to evaluate the biomechanical parameters (mass, stiffness and damping) of the proposed model. Based on the optimized parameters, segmental transmissibilities are calculated and compared with experimental readings. The proposed model is then combined with a 7-dofs commercial car model to perform a ride comfort study. The ISO 2631-1:1997 ride comfort standards are used to compare the simulated segmental accelerations. Additionally, the influence of biomechanical parameters on most critical organs is analyzed to improve ride comfort. The outcomes of the analysis reveal that seated occupants perceive maximum vibration in the 3–6 Hz frequency range. To improve seated occupants' ride comfort, automotive designers must concentrate on the pelvis region. The adopted methodology and outcomes are helpful to evaluate protective measures in automobile industries. Furthermore, these procedures may be used to reduce the musculoskeletal disorders in seated occupants.

Multiobjective Demand Double-Layer Energy Consumption Optimization Strategy for Microgrid Based on Improved HPSOFA

In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. The proposed improved algorithm introduces a random perturbation term and adaptive learning coefficients, and the algorithm is selected by the dominance relationship of individuals, which improves the diversity of populations and increases the possibility of the algorithm to solve the global optimum. The proposed algorithm is used to solve the test functions of different dimensions, and the results show that the Pareto front of HPSOFA has better distribution and accuracy, which verifies the effectiveness of the proposed algorithm. Simulation analysis is carried out using power data from a region of Liaoning Province, China, and experiments conclude that the operating cost and environmental cost of HPSOFA are significantly lower than other algorithms.

Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery.

In recent years, deep convolutional neural networks (DCNNs) have been widely used in the task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast storage and computational cost of DCNN limits its application to spaceborne or airborne onboard devices with limited resources. In this paper, a set of lightweight detection networks for SAR ship target detection are proposed. To obtain these lightweight networks, this paper designs a network structure optimization algorithm based on the multi-objective firefly algorithm (termed NOFA). In our design, the NOFA algorithm encodes the filters of a well-performing ship target detection network into a list of probabilities, which will determine whether the lightweight network will inherit the corresponding filter structure and parameters. After that, the multi-objective firefly optimization algorithm (MFA) continuously optimizes the probability list and finally outputs a set of lightweight network encodings that can meet the different needs of the trade-off between detection network precision and size. Finally, the network pruning technology transforms the encoding that meets the task requirements into a lightweight ship target detection network. The experiments on SSDD and SDCD datasets prove that the method proposed in this paper can provide more flexible and lighter detection networks than traditional detection networks.

Multi-objective firefly algorithm combining Logistic map-ping and cross-variation

Multi-objective firefly algorithm combining Logistic map-ping and cross-variation

A multi-objective firefly algorithm combining logistic mapping and cross-variation

A multi-objective firefly algorithm combining logistic mapping and cross-variation

An improved multi-objective firefly algorithm for energy-efficient hybrid flowshop rescheduling problem

Hybrid flowshop scheduling problem is a hot research topic, and is widely applied for production shop or line in chemical industry, metallurgical industry, semiconductor manufacturing and other industries. However, on the one hand, the uncertain events are inevitable in actual production, which will disrupt the production plan. On the other hand, nowadays the energy problem becomes more and more serious, and attracts much attention in the manufacturing industry. Therefore, an energy-efficient hybrid flowshop rescheduling problem under the machine breakdown is addressed in this paper. Firstly, the mathematical model for the problem is established, and an energy saving strategy based on problem model is designed, which can ensure the reduction of energy consumption without affecting the production efficiency. Then, an improved multi-objective firefly algorithm is proposed to optimize the production efficiency, energy consumption and production stability. To express the problem characteristics, a two-level encoding mechanism is used to describe the individual, and a corresponding decoding mechanism is presented to generate the scheduling scheme. By simulating the location updating law of the fireflies, the population updating rule is designed, in which the variable neighborhood search is employed to avoid the local optimal. To ensure the quality of the solution set, the fast non-dominated sorting method and elite individual reserving strategy are introduced to the population evolution. Finally, the numerical experimental results indicate that the designed energy saving strategy is effective, and the proposed algorithm obtains better Pareto frontier and performs the better convergence and diversity comparing with MOEA/D and NSGA-Ⅱ, the common algorithms to solve complex multi-objective optimization problem.

AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.

Multi-body modelling and ride comfort analysis of a seated occupant under whole-body vibration

Seated occupants of any on/off-road vehicle are continuously exposed to undue low-frequency mechanical whole-body vibration (WBV) owing to the vibration conveyed from seat and backrest because occupants need to compromise with comfort and safety, leading to several musculoskeletal disorders and injuries in the body. Pain in the lumbar region is one of the most common problems that develop because of force transmitted from the backrest. Thus, consideration of backrest is highly essential while developing a biomechanical model for ride comfort analysis. For this purpose, a 20 degrees-of-freedom (dofs) biomechanical model has been proposed with a backrest to analyse cross-coupled segmental transmissibility. The interconnection between different segments of mass and backrest are modelled using eight linearized (direct as well as cross-coupled) stiffness and damping parameters. Biomechanical model parameters are estimated and optimized through a multi-objective firefly algorithm by minimizing the sum of squares error difference between analytical results and experimental segmental transmissibility published in the literature. Subsequently, ride comfort analysis is performed by integrating the proposed model with 7-dofs passenger car model. For different body segments, simulated results are obtained in terms of vertical accelerations and compared with ride comfort standards as per ISO 2631-1:1997. Additionally, a sensitivity analysis (±20% variation in mass, stiffness and damping) is performed to enhance the ride comfort of the occupant. The findings suggest that designers must focus on the pelvis, lower and upper back regions to improve the ride comfort of the occupants. The concepts and ideas outlined in this article are directly applicable in making human dummies, car suspension and seat design in the industry.

Multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity

Balancing the convergence and diversity in the multi-objective firefly algorithm is essential for obtaining high precision and well distributed Pareto front. However, most existing algorithms cannot​ guarantee such balance, leading to a poor comprehensive performance. To address this limitation, this paper proposes a multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity (MEFA-CD). Firstly, an improved linear congruence method is used to generate the initial population with uniform distribution, to provide a good start for the subsequent population evolution and ensure the global search ability; Secondly, a hybrid learning strategy is utilized to identify the best elite solution according to the maximum fitness value. Combined with the current best solution, the firefly is guided to learn under the effect of compensation factor. On the one hand, it breaks through the population constraints, which yields a faster convergence to the Pareto optimal solution set. On the other hand, it expands the search range of the population, which improves the diversity and the accuracy of the Pareto optimal set; Finally, the crowding distance mechanism is used to delete the aggregation solution, which maintains the diversity of external files and ensures the local development ability of the population, and further improves the convergence of the algorithm. Experimental results show that, compared with other multi-objective optimization algorithms, the proposed algorithm has better performance in convergence and diversity, among which the optimization performance is improved by 61% compared with the standard MOFA.

Post-Disaster Temporary Shelters Distribution after a Large-Scale Disaster: An Integrated Model

This paper develops an integrated model for the distribution of post-disaster temporary shelters after a large-scale disaster. The proposed model clusters impacted areas using an Adaptive Neuro-Fuzzy Inference System (ANFIS) method and then prioritizes the points of clusters by affecting factors on the route reliability using a permanent matrix. The model’s objectives are to minimize the maximum service time, maximize the route reliability and minimize the unmet demand. In the case of ground relief, the possibility of a breakdown in the vehicle is considered. Due to the disaster’s uncertain nature, the demands of impacted areas are considered in the form of fuzzy numbers, and then the equivalent crisp counterpart of the non-deterministic is made by Jimenez’s method. Since the developed model is multi-objective, the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Firefly Algorithm (MOFA) are applied to find efficient solutions. The results confirm higher accuracy and lower computational time of the proposed MOFA. The findings of this study can contribute to the growing body of knowledge about disaster management strategies and have implications for critical decision-makers involved in post-disaster response projects. Furthermore, this study provides valuable information for national decision-makers in countries with limited experience with disasters and where the destructive consequences of disasters on the built environment are increasing.

Data Dissemination in VANETs Using Clustering and Probabilistic Forwarding Based on Adaptive Jumping Multi-Objective Firefly Optimization

Data dissemination in a VANETs network requires a meticulous process to ensure a high quality of service and eliminate hazardous conditions due to congestion or a broadcast storm. Considering multi-metric approaches and their implicit conflicting nature, it is necessary to handle this through effective multi-objective optimization algorithms. An effective optimization can be handled using a meta-heuristic approach with a high level of solution interactions. For this purpose, firefly was selected, which is a type of meta-heuristic search algorithm. Several developments of the firefly optimization were added to increase its capability to find more dominating solutions, namely, objective decomposition, archive management, and controlled mutation for exploration and exploitation balance. This developed multi-objective optimization was designated as adaptive jumping multi-objective firefly algorithm (AJ-MOFA). Afterwards, AJ-MOFA was integrated with a clustering and forwarding mechanism (CFM). This mechanism includes three main components. The first is clustering, which uses arbitration based on the cluster head score; the second is a forwarding mechanism that uses probabilistic forwarding and the third is AJ-MOFA. The solution space design in CFM combined two variables: the first is the probability of forwarding and the second is the maximum number of nodes within one cluster. The metrics to be incorporated in the multi-objective optimizations are the packet delivery ratio (PDR), the end-to-end delay (E2E-delay) and the number of dropped packets. Comparing both AJ-MOFA and CFM with benchmarks using multi-objective optimization and networking metrics reveals the superiority in most evaluation measures, which makes them promising algorithms for data dissemination in VANETs. The results showed an accomplished PDR of 60% and an E2E delay of 6.6 seconds, while the number of dropped packets was almost nine for the entire running time of the experiment, comparing a similar or lower performance of the benchmarks for these metrics.

Integrated Optimization Method of IPPS under TOU and Tiered Electricity Price

Energy-saving production is one of the issues that must be paid attention to by today’s manufacturing enterprises. Aiming at the problem of integrated process planning and scheduling (IPPS) in the manufacturing process, considering Time-of-Use (TOU) and tiered electricity price, this paper systematically studies the energy-saving scheduling problem in order to reduce the power consumption in processing and production. To establish the multiobjective optimization mathematical model of the problem, the load balancing problem of the equipment is considered, the minimization of the power consumption and the maximum load of the equipment are taken as the optimization objectives. Then, considering the constraints of resource and multiprocess, the switching strategy of the equipment in idle time is introduced, including shutdown and restart operations. In order to solve the model easily, a multiobjective firefly algorithm (MOFA) based on five-layer coding is designed, and the elite strategy is introduced to protect the excellent firefly individuals in the iterative population. Finally, through a specific example, the Pareto solution set is obtained, which provides a reference scheme for decision-makers, and verifies the correctness of the model and the effectiveness of the algorithm.

CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis

In this manuscript, a Chaotic Multi Verse Harris Hawks Optimization algorithm based Deep Kernel Machine Learning Classifier (CMVHHO-DKMLC) method is proposed for medical diagnostics. By using CMVHHO, the feature selection (FS) is done for finding ideal feature subset of medical documents. In the proposed method, to obtain the ideal subset of characteristics for best classification, initially chaotic multi-verse optimization (CMV) was implemented to create first positions, after that Harris Hawks optimization (HHO) was utilized to keep positions informed current population at DKMLC-based search space. Then the proposed model is performed on MATLAB and the performance of proposed method is evaluated with assessment metrics. For demonstrating the efficacy of the proposed system on medical diagnosis, this manuscript carries out a series of comparative studies by testing with two medical data sets, i.e. Wisconsin Breast Cancer Database [40] and PIMA Indian Diabetic Dataset [41]. This proposed CMVHHO-DKMLC classifier provides 1.165% and 0.667% higher accuracy value compared with existing classifier model like against the chaotic multi-swarm whale optimizer-boosted support vector machine (CMWOAFS-SVM) and improved gray wolf optimization-based feature selection wrapped kernel extreme learning machine (IGWO-KELM) for breast cancer diagnosis. Similarly the proposed CMVHHO-DKMLC classifier provides 25.641% and 3.55% higher accuracy value compared with existing classifier model like chaotic multi-swarm whale optimizer-boosted support vector machine (CMWOAFS-SVM) and multi-objective firefly and multi-objective imperialist competitive algorithm optimizer-boosted support vector machine (MOFA-MOICA-SVM) for diabetic diagnosis. Simulation outcomes have demonstrated the dominance of proposed technique over other two competitive methods.

Pareto-based Multi-objective Firefly Algorithm with Hierarchical Clustering for Environmental-Economic Power Dispatch Problem

This paper proposes a multi-objective firefly algorithm (MOFFA) to solve the environmental-economic power dispatch (EEPD) problem. Also, it presents a modified firefly algorithm (FFA) to solve the economic power dispatch (EPD) and the emission dispatch (ED) problems as single goals. The modifications proposed on the traditional FFA aim to improve its exploration and ensure the feasibility of the obtained solutions. The proposed MOFFA uses an external Pareto set to keep the non-dominated solutions, where a hierarchical clustering algorithm is used to get a representative and controlled set of Pareto-optimal solutions. The best compromise solution is also extracted from the Pareto set using an approach based on the fuzzy set theory. The constraints of EEPD, EPD, and ED problems are power balance constraint, generation limits constraint, and transmission power losses. To verify the effectiveness of the proposed algorithms, two methodologies are adopted and tested on the IEEE 30-bus test and the 10-unit test system with valve-point loading. In methodology 1, the EPD problem and the ED problem are solved separately using the modified FFA. In methodology 2, the EEPD is solved as a true multi-objective optimization problem using the proposed MOFFA. The simulation results and the statistical analysis ensure the high-quality solutions of the proposed algorithms and prove the ability of the MOFFA to produce well-distributed Pareto-optimal solutions.

Multi-objective optimization of GFRP injection molding process parameters, using GA-ELM, MOFA, and GRA-TOPSIS

Owing to the influence of the injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by glass fiber-reinforced plastic (GFRP) injection molding. To minimize these two defects, an extreme learning machine optimized with a genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA), and a multi-objective decision-making method (GRA-TOPSIS) were implemented in this study. All of the experiments, based on Latin hypercubic sampling (LHS), were conducted using Moldflow software to obtain the results for warpage and volume shrinkage. The prediction accuracy of the defect-prediction models based on the extreme learning machine (ELM) and GA-ELM algorithms were compared. The results show that the GA-ELM models can better predict the defect values. Finally, MOFA was used to find the Pareto optimal front, and the GRA-TOPSIS method was used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage were effectively reduced by 12.25% and 6.11%, respectively, compared with before optimization, which indicates the effectiveness and reliability of the optimization method.

Automating the mixture design of lightweight foamed concrete using multi-objective firefly algorithm and support vector regression

Lightweight concrete (LWC) is widely used in the construction industry due to a variety of advantages. However, compared with traditional normal-weight concrete, more influencing variables (e.g. types of lightweight aggregates) must be considered to optimize multiple properties including uniaxial compressive strength (UCS), density and cost. This makes the mixture design of LWC more difficult or sometimes impossible using laboratory experiments. To address this issue, this study proposes a multi-objective optimization (MOO) method using machine learning and metaheuristic approaches for LWC mixture design through a two-step approach. In the first step, a least squares support vector regression (LSSVR) model is constructed to predict multiple properties of LWC. The hyper-parameters of the LSSVR model are tuned using the firefly algorithm (FA). A dataset containing a large number of different mixtures of LWC is compiled from published literature. High prediction accuracy (0.97 for UCS and 0.90 for density) is achieved on the test dataset (including 30% of all the instances). In the second step, a newly developed multi-objective FA (MOFA) model is used to optimize the LWC mixture, while satisfying the constraints. The Pareto fronts of the triple objectives (UCS, cost and density) are successfully obtained. The proposed MOO method is powerful and efficient in finding optimal LWC mixtures with conflicting objectives and therefore decision making can be facilitated in early phases of construction.