Multi-objective Cuckoo Search Research Articles

Geometrical and material optimization of the functionally graded doubly-curved shell by metaheuristic optimization algorithms

This paper presents the simultaneous geometrical and material optimization of functionally graded doubly-curved shells under static load and free vibration. The weak form of governing equations is derived from third-order shear and normal deformation theory. Bending and free vibration analyses were conducted using the finite element method. The functionally graded material (FGM) is composed of metal and ceramic, and Voigt’s rule of mixtures is applied to capture the characteristics of FGMs throughout the shell thickness direction. In this regard, two multi-objective optimization algorithms (i.e., multi-objective particle swarm optimization and multi-objective cuckoo search optimization algorithms are employed. The objective functions combine minimizing the maximum normalized displacement and mass and maximizing the first dimensionless frequency. The results show that where minimizing the mass of the FG shell structure was considered an objective function, the value of the power index is equal to zero for almost all studied cases.

A Multi-Objective Trajectory Planning Method of the Dual-Arm Robot for Cabin Docking Based on the Modified Cuckoo Search Algorithm

During the assembly of mechanical systems, the dual-arm robot is always used for cabin docking. In order to ensure the accuracy and reliability of cabin docking, a multi-objective trajectory planning method for the dual-arm robot was proposed. A kinematic model of the dual-arm robot was constructed based on the Denavit–Hartenberg (D-H) method firstly. Then, in the Cartesian space, the end trajectory of the dual-arm robot was confirmed by the fifth-order B-spline curve. On the basis of a traditional multi-objective cuckoo search algorithm, a modified cuckoo algorithm was built using the improved initial population generation method and the step size. The total consumption time and joint impact were selected as the objective functions, the overall optimal solution for the modified cuckoo algorithm was obtained using the normalized evaluation method. The optimal trajectory planning was achieved. Finally, the feasibility and effectiveness of the trajectory planning method were verified with the experiments.

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

This paper establishes a location-routing optimization model of the distribution center for emergency supplies with the goals of system reaction time, total cost of consumption, psychological fear of the populace in disaster-affected locations, and material usage rate. Where the excess time, demand, and penalty coefficient are the components of the penalty cost in the total consumption cost, and where the psychological panic of those in the affected area is represented by the psychological perception function of panic developed in accordance with the prospect theory. An improved hybrid multi-objective cuckoo-large-neighborhood search algorithm was then designed to introduce tent mapping, nonlinear inertia weights, elite strategies, congestion operators, and dynamically adjusted discovery probabilities into the standard multi-objective cuckoo optimization algorithm, which generates a new solution using a large-neighborhood search algorithm after discarding part of the solution with the discovery probability, and then accepts the current nondominated solution with dynamic probabilities. The paper uses the improved algorithm to solve Christofides69, an arithmetic example from the standard dataset of the LRP problem, and the results show that the solution provided by the improved algorithm outperforms the solutions provided by the standard multi-objective cuckoo search algorithm and the NSGA-II algorithm in terms of the total cost of dissipation, the level of psychological panic of the people in the affected area, the rate of utilization of the supplies, and the number of distribution centers open. Finally, the improved algorithm was used to analyze cases of different sizes separately, and it was found that the algorithm yielded better results and was therefore able to demonstrate its effectiveness.

A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor

Existing multi-objective cuckoo search (MOCS) algorithms are based on either Pareto dominance or decomposition. However, when dealing with complex multi-objective problems (MOPs), the Pareto dominance-based algorithms face a decrease in selection pressure, and the decomposition-based algorithms easily gain poor distributions. The objective of this paper is to repurpose an indicator-based MOCS by combining improved diversity enhancement (IDE) and adaptive scaling factor (ASF) for MOPs. In the proposed algorithm, hypervolume is used as the indicator to guarantee better convergence and enough spread of the population. IDE chooses the large hypervolume to rebuild the parent population to compensate for the lack of population diversity. Additionally, ASF makes full use of individuals information to enhance the search ability of Lévy component in cuckoo search. Comprehensive experiments on 31 benchmark functions including two classical suites ZDT, WFG, and one challenged suite proposed in CEC2019, as well as 8 real-world problems were conducted to test the proposed algorithm. Compared with several state-of-the-art multi-objective evolutionary algorithms, the effectiveness and efficiency of our proposed method were demonstrated by the results.

Comparative analysis of two cogeneration systems with different connection modes

This study explores the differences between two novel cogeneration systems with different connection modes. These two systems are the series and parallel systems composed of the Brayton cycle, the organic flash cycle, and the organic Rankine cycle, respectively, which both recover flue gas waste heat and LNG cold energy. First, the equipment and overall system performance of the series system and the parallel system are compared from three aspects of thermodynamics, economy, and exergoenvironment to evaluate the basic performance of the two systems under initial conditions. Next, the performance variations of both systems are assessed by varying the compressor outlet pressure, flash pressure, R14 mass flow rate, and heat source temperature. Finally, the Multi-Objective Cuckoo Search algorithm is used to optimize the systems for two and three objectives, taking the exergy efficiency, payback period and product unit exergoenvironmental impact as objective functions. The final results indicate that the three-objective optimized parallel system has the best performance, the exergy efficiency, payback period, and product unit exergoenvironmental impact of that is 62.25%, 1.389 years, and 7.799 × 10−3 mpt/kJ, respectively. By comparing the performance differences between the series system and the parallel system, this study provides application ideas for engineering requirements.

Enhanced Disease Detection Using Contrast Limited Adaptive Histogram Equalization and Multi-Objective Cuckoo Search in Deep Learning

Enhanced Disease Detection Using Contrast Limited Adaptive Histogram Equalization and Multi-Objective Cuckoo Search in Deep Learning

Optimal Capacity and Location for STATCOM with Seasonal Wind Power Prediction Using C-Vine Copula

This paper proposes a selection strategy for determining the optimal capacity and location of a static synchronous compensator (STATCOM) device under intermittent output variations of renewable energy sources using a multi-objective cuckoo search (MOCS) algorithm. The impact of seasonal output variations on the location and capacity of the STATCOM devices was analyzed based on the daily output variations of the wind turbines. Three objective functions were considered to determine the optimal location and capacity of the STATCOM devices: (1) minimizing the system line losses, (2) minimizing the transient voltage indicators, and (3) minimizing the total cost of the STATCOM capacity. MOCS was combined with the Pareto front to determine the non-dominated solution sets for the four seasons. The Manhattan distance method was used to select the most suitable installation locations and capacities within a specified time interval. Power flow calculations were performed using DIgSILENT Power Factory 2021 and MATLAB R2021b. To validate the proposed method, a test experiment was conducted using an IEEE 39 bus system.

Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC.

Dynamic optimization of a wastewater treatment process for sustainable operation using multi-objective genetic algorithm and non-dominated sorting cuckoo search algorithm

Wastewater treatment plants (WWTP) face challenges due to nonlinearity and influent oscillations. An efficient optimal control system is necessary to improve effluent water quality without increasing operating costs. This research proposes a novel dynamic optimization control system using a non-dominated sorting-based multi-objective cuckoo search optimization algorithm (NSMOCS). Benchmark Simulation Model 1 (BSM1) was used as the WWTP model. Two sets of objectives were analyzed: the first set consists of the effluent quality index (EQI) and operating cost index (OCI), and the second comprises the reduction in nitrogen and ammonia concentrations at the WWTP. A Pareto front was obtained by evaluating these objectives using a multi-objective genetic algorithm (MOGA). Contribution analysis was used to find the individual weight values of these objectives. Furthermore, two sets of multi-objective cost functions were devised using weighted sum analysis. By minimizing the above-mentioned cost functions with NSMOCS algorithm, the best tuning parameters for the proportional integral (PI) controller were found. The simulation results with this proposed control strategy showed that the pollution units per day and the operating cost index were reduced during dry, rain, and storm weather conditions, with reductions of 1.3 %, 0.77 %, 0.91 %, and 0.9 %, 0.85 %, and 0.63 %, respectively.

Simultaneous aerodynamic and structural optimisation of a low-speed horizontal-axis wind turbine blade using metaheuristic algorithms

AbstractThis work presents a concurrent design and multi-objective optimisation framework of horizontal axis wind turbine blades, made of composite material, for low wind speed. The optimisation model aims to minimise the structural mass of the blade whilst simultaneously maximising the turbine power output, subjected to three constraints viz. blade tip deflection, and Tsai-Hill and von Mises criteria. The design variables are blade shape and details of the internal blade structure. The control points and polynomial interpolation technique were adopted to determine the blade shape while the airfoil types at blade sections remained fixed. The internal blade structure design variables include the thickness of ribs and spars and the carbon fibre thickness and orientations. The blade element momentum approach is utilised to calculate turbine power and structural loads, whereas a finite element method is employed for structural analysis. Twelve multi-objective metaheuristics algorithms are used to solve the proposed multi-objective optimisation problem while their performance is investigated. The results obtained show that the multi-objective cuckoo search algorithm is the most efficient method. This study is said to be the baseline for a future study on multi-objective optimisation which combines two design stages of the composite low-speed wind turbine blades.

Life‐cycle cost‐oriented multiobjective optimization of composite frames considering the slab effect

SummaryThe life‐cycle cost‐oriented design philosophy is a promising tool for building resilient cities as it helps in gaining insights into the impact of hazard‐induced damage and repair of civil and infrastructure systems. In this study, a socioeconomic parameter‐independent practical formulation was introduced for life‐cycle cost analysis by combining the economic loss rate associated with different damage limit states and cloud analysis‐based probabilistic seismic demand model. A framework for life‐cycle cost analysis‐based seismic design optimization was proposed using an emerging nature‐inspired algorithm, namely, the multiobjective cuckoo search. By considering an eight‐story prototype composite frame, the framework was used to determine the trade‐off design alternatives between competing optimization objectives. Conventional and improved fiber models were developed to comparatively evaluate the influence of the slab spatial composite effect on Pareto optimal designs. The key drivers of change in three cost indicators were identified using generalized linear models. The result indicates that the overstrength factor is the critical design parameter affecting the initial construction, seismic damage, and life‐cycle costs, with statistical significance at the 0.001 level.

Optimal control of sewage treatment process using a dynamic multi-objective particle swarm optimization based on crowding distance

A variety of multi-objective optimization algorithms has been extensively investigated in the past decades to tackle the optimal decision of sewage treatment process. However, achieving the ideal solutions is challenging in the multi-criteria decision-making process from Pareto optimal sets due to the complicated relationships among influencing factors, especially in the case of large decision variables involved in the wastewater treatment process. We thus proposed an improved dynamic multi-objective particle swarm optimization algorithm based on crowding distance (DMOPSO-CD) to obtain global optimal solutions for the balance between energy consumption (EC) and effluent quality (EQ) in sewage treatment processes. The algorithm consists of optimization modules and a self-organizing fuzzy neural network, improving the global searching ability of particles, maintaining the diversity of non-inferior solutions, and solving the multi-objective vital issues in the optimization of sewage treatment process. The proposed optimization algorithm was applied to benchmark simulation model No.1, and the optimization results showed that the EC for wastewater treatment in dry, rainy, and storm weather was reduced by 7.87%, 6.28%, and 7.30%, respectively. This methodology outperformed several widely applied algorithms, including the multi-objective cuckoo search, non-dominated sorting genetic algorithm-II, and improving Pareto evolutionary algorithm in terms of EQ and EC, which opens a new window for the optimal decision of sewage treatment.

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms.

Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.

Dynamic virtual cellular reconfiguration for capacity planning of market-oriented production systems

<p style='text-indent:20px;'>Market-oriented production systems generally have the characteristics of multi-product and small-batch production. Dynamic virtual cellular manufacturing systems create virtual manufacturing cells periodically in a planning horizon to respond to changing demands flexibly and quickly, and thus are suitable for production planning problems of market-oriented production systems. In the current research, we propose a dynamic virtual cell reconfiguration framework under a dynamic environment with unstable demands and multiple planning cycles. In this framework, we formulate a two-phase dynamic virtual cell formation (DVCF) model. In the first phase, the proposed model aims to maximize processing similarity and balance the workload in the system. In the second phase, we consider the objective of reconfiguration stability based on the first phase model. To address the proposed model, we design a hybrid metaheuristic named Lévy-NSGA-Ⅱ, and perform various computational experiments to examine the performance of the proposed algorithm. Results of experiments indicate that the proposed Lévy-NSGA-Ⅱ based algorithm outperforms multi-objective cuckoo search (MOCS) and NSGA-Ⅱ in solution quality and optimal solution size.</p>

Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification.

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose bands based on band correlation and information has become more significant, but also complicated. Band selection is a combinatorial optimization problem, and intelligent optimization algorithms have been shown to be crucial in solving combinatorial optimization problems. However, major of them only use a single objective as the selection index, while neglecting the overall features of hyperspectral images, which may lead to inaccuracy in object detection. To tackle this, we propose a band selection method based on a multi-objective cuckoo search algorithm (MOCS) when constructing a multi-objective unsupervised band selection model based on the amount of information and correlation of the bands (MOCS-BS). Specifically, an adaptive strategy based on population crowding degree is first proposed to assist Lévy flight in overcoming the influence of the parameter constancy. Then, an information-sharing strategy based on grouping and crossover is designed to balance the search ability between global exploration and local exploitation, which can overcome the shortcomings caused by the lack of information interaction between individuals. Finally, the HSI classification experiments are performed by Random Forest and KNN classifiers based on the subset of bands selected by the proposed MOCS-BS method. The proposed method is compared with state-of-the-art algorithms including neighborhood grouping normalized matched filter (NGNMF) and multi-objective artificial bee colony with band selection (MABC-BS) on four HSI datasets. The experimental results demonstrate that MOCS-BS is more effective and robust than other methods.

A Process Parameter Predictive Framework for Laser Cladding of Multi-principal Element Alloys

• A framework is constructed to predict the processing parameters for laser additive manufacturing of MPEAs • A set of dimensionless objective functions correlated elemental diffusion and atomic radii to the macroscopic process parameters. • A multi-objective cuckoo search optimization enables a Pareto front to identify optimal processing parameters. Surface engineering by additive manufacturing of multi-principal element alloys (MPEAs) has generated significant attention recently for the range of remarkable material properties that can be achieved. A challenge exists in determining the optimum processing parameters for fabricating alloys of various compositions, as they govern the quality of the deposited material. Nevertheless, only limited models are available to predict the initial parameter window for the processing parameters. Using AlCoCrFeNi MPEA as a testbed for laser metal deposition, we present a framework correlating material properties to processing variables coupling predictions from fundamental molecular simulations and meta-heuristic optimization approaches. A set of dimensionless objective functions are constructed to connect elemental diffusion and atomic radii to the macroscopic process parameters, viz., cooling rate, energy density and powder deposition density. Our results suggest that the diffusion coefficient varies exponentially with cooling rate, when the MPEA assumes a crystalline phase upon solidification due to the formation of crystal point defects and a high activation energy rate required for diffusion during rapid cooling. However, the absence of these defects in an amorphous phase of the alloy renders no definitive correlation for the elemental diffusion coefficients with varying cooling rates. Through a multi-objective cuckoo search optimization, we construct a Pareto front to identify optimal values for processing variables, which concur with the parameters adopted in the literature for laser cladding of complex alloys.

Elite-guided multi-objective cuckoo search algorithm based on crossover operation and information enhancement

In recent years, the cuckoo search (CS) algorithm has been successfully applied to single-objective optimization problems. But in real life, most optimization problems are multi-objective optimization problems (MOPs). In order to enable CS to better solve MOPs, this paper proposes an elite-guided multi-objective cuckoo search algorithm based on cross-operation and information enhancement (CIE-MOCS). This algorithm first enhances its population diversity through crossover operation, then adds elite individuals to guide its update process to speed up the algorithm convergence speed. Finally, the method of information enhancement is adopted in the abandonment process, so that the algorithm is not easy to fall into the local optimum. In order to verify the performance of the algorithm, this paper uses a variety of benchmark functions and performance evaluation indicators to evaluate it, and provides a case to verify the effectiveness of the algorithm in practical applications. The experimental results show that CIE-MOCS has good performance compared with the contrasting algorithms.

An Effective Secured Dynamic Network-Aware Multi-Objective Cuckoo Search Optimization for Live VM Migration in Sustainable Data Centers

With the increasing use of cloud computing by organizations, cloud data centers are proliferating to meet customers’ demands and host various applications using virtual machines installed in physical servers. Through Live Virtual Machine Migration (LVMM) methods, cloud service providers can provide improved computing capabilities for server consolidation maintenance of systems and potential power savings through a reduction in the distribution process to customers. However, Live Virtual Machine Migration has its challenges when choosing the best network path for maximizing the efficiency of resources, reducing consumption, and providing security. Most research has focused on the load balancing of resources and the reduction in energy consumption; however, they could not provide secure and optimal resource utilization. A framework has been created for sustainable data centers that pick the most secure and optimal dynamic network path using an intelligent metaheuristic algorithm, namely, the Network-aware Dynamic multi-objective Cuckoo Search algorithm (NDCS). The developed hybrid movement strategy enhances the search capability by expanding the search space and adopting a combined risk score estimation of each physical machine (PM) as a fitness criterion for ensuring security with rapid convergence compared to the existing strategies. The proposed method was assessed using the Google cluster dataset to ascertain its worthiness. The experimental results show the supremacy of the proposed method over existing methods by ensuring services with a lower total migration time, lower energy consumption, less makespan time, and secure optimum resource utilization.

Multiobjective Oriented Task Scheduling in Heterogeneous Mobile Edge Computing Networks

6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks.

Coordinated control strategy for braking and shifting for electric vehicle with two-speed automatic transmission

This paper presents a coordinated control strategy for braking and shifting of electric vehicles that are equipped with a two-speed automatic transmission. The coordinated control strategy mainly includes braking force distribution and synthetic shifting regulation for braking operations. Firstly, the vehicle model is established for investigation of the shift process during braking. Then, the braking force distribution considering energy efficiency is presented, and the regenerative braking force is calculated. With the target of maximizing energy recovery and ensuring driving comfort, an optimal shift schedule during regenerative braking is advanced based on the multi-objective cuckoo search algorithm. Due to the interaction of regenerative braking force in torque releasing and restoring process, an adaptive braking mode switching control is proposed to improve the shifting smoothness. The detailed simulations and hardware-in-the-loop validations are conducted to verify the anticipated performance of the proposed strategy.