Multi-objective Search Algorithm Research Articles

A Study on Site Selection for Regional Air Rescue Centers Based on Multi-Objective Jellyfish Search Algorithm

In recent years, air emergency rescue capabilities have become increasingly important as an indicator of national comprehensive strength and development status. Air emergency rescue performs an indispensable role in addressing social emergencies by virtue of its fast response capabilities and extensive coverage. This vital aspect of emergency response ensures the timely deployment of rescue personnel and resources, enabling efficient operations in diverse and often challenging environments. To enhance regional emergency response capabilities, this paper presents a novel siting model that overcomes the limitation of single-objective approaches by integrating multiple objectives and considering the synergistic effects of network nodes, and the corresponding efficient solving algorithm is designed for this model. First, a multi-objective optimization function is established that fully incorporates the construction cost of the rescue station, response time, and radiation range. A radiation function is developed to evaluate the degree of radiation for each candidate airport. Second, the multi-objective jellyfish search algorithm (MOJS) is employed to search for Pareto optimal solutions of the model using MATLAB tools. Finally, the proposed algorithm is applied to analyze and verify the site selection for a regional air emergency rescue center in a certain region of China, and ArcGIS tools are used to draw the site selection results separately by prioritizing the construction cost under different numbers of site selection points. The results demonstrate that the proposed model can achieve the desired site selection goals, thus providing a feasible and accurate method for future air emergency rescue station selection problems.

Robust Optimization Design of the Aerodynamic Shape and External Ballistics of a Pulse Trajectory Correction Projectile

To improve the tactical and technical performance of pulse correction projectiles while maintaining stability in uncertain conditions and considering practical engineering constraints, this study performs a multi-objective robust optimization design of the aerodynamic shape and external ballistics of a projectile. The study utilizes an aerodynamic force engineering algorithm and numerical trajectory calculations to obtain the projectile’s performance responses within the Latin hypercube design space. To enhance optimization efficiency, a stochastic Kriging surrogate model is established to capture the inherent uncertainty of limited input data. Ultimately, a Pareto optimal solution for the projectile is obtained using a non-dominated sorting multi-objective sparrow search algorithm. The results of this study demonstrate that the consideration of design uncertainty in the robust optimization of pulse correction projectiles leads to significant enhancements in both lateral correction ability and range while satisfying flight stability requirements. Moreover, when compared to deterministic optimization, the performance variability of the design is markedly improved. This research methodology provides valuable insights for optimizing the performance of pulse correction projectiles.

Automatic clustering and feature selection using multi-objective crow search algorithm

Today’s real-world data is frequently significant in size, with many redundant, missing, and noise-based features and data instances must be addressed before applying various data-mining-based algorithms for further knowledge discovery. Excessive dimensionality may be mitigated by carefully excluding unnecessary characteristics and selecting a reasonable subset of features. When presented as an optimization issue, choosing the best clusters using the most suitable subset of attributes is a challenge that may be handled using practical meta-heuristic approaches. Besides this, the automatic finding of the appropriate cluster number is another challenging task for the real-world dataset in the unsupervised machine-learning study. The present work proposes a multi-objective crow search algorithm for clustering and feature selection (MO-CSACFS) by modifying the crow search algorithm and introducing a levy flight-based two-point cross-over mechanism for a better exploration phase of the crow and further making it suitable for multi-objective optimization problems. MO-CSACFS addresses both issues using the three objective functions to find appropriate cluster numbers and features. MO-CSACFS is implemented over several real-life and synthetic datasets with varying instances, features, and cluster numbers to assess the algorithm’s performance; apart from that, the present work is also applied over several gene-expression datasets. MO-CSACFS is compared with two similar recently proposed multi-objective optimization processes used over an automatic, unsupervised machine learning task. The results show that the MO-CSACFS has produced a compact and robust cluster comparable to other similar works from the literature.

Minimizing the makespan and carbon emissions in the green flexible job shop scheduling problem with learning effects

One of the most difficult challenges for modern manufacturing is reducing carbon emissions. This paper focuses on the green scheduling problem in a flexible job shop system, taking into account energy consumption and worker learning effects. With the objective of simultaneously minimizing the makespan and total carbon emissions, the green flexible job shop scheduling problem (GFJSP) is formulated as a mixed integer linear multiobjective optimization model. Then, the improved multiobjective sparrow search algorithm (IMOSSA) is developed to find the optimal solution. Finally, we conduct computational experiments, including a comparison between IMOSSA and the nondominated sorting genetic algorithm II (NSGA-II), Jaya and the mixed integer linear programming (MILP) solver of CPLEX. The results demonstrate that IMOSSA has high precision, good convergence and excellent performance in solving the GFJSP in low-carbon manufacturing systems.

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.

A Multi-Objective Crow Search Algorithm for Influence Maximization in Social Networks

Influence maximization is a key topic of study in social network analysis. It refers to selecting a set of seed users from a social network and maximizing the number of users expected to be affected. Many related research works on the classical influence maximization problem have concentrated on increasing the influence spread, omitting the cost of seed nodes in the diffusion process. In this work, a multi-objective crow search algorithm (MOCSA) is proposed to optimize the problem with maximum influence spread and minimum cost based on a redefined discrete evolutionary scheme. Specifically, the parameter setting based on the dynamic control strategy and the random walk strategy based on black holes are adopted to improve the convergence efficiency of MOCSA. Six real social networks were selected for experiments and analyzed in comparison with other advanced algorithms. The results of experiments indicate that our proposed MOCSA algorithm performs better than the benchmark algorithm in most cases and improves the total objective function value by more than 20%. In addition, the running time of the MOCSA has also been effectively shortened.

An Optimal Multi-Objective Approach for Distributed Generation and AVR Placement in Distribution Networks Using IPMBSA

Inclusion of automatic voltage regulators (AVRs) and distributed generation units (DGs) in the distribution system (DS) will enhance the system performance if allocated optimally. Optimal allocation of AVRs and DGs is a complex combinatorial problem requiring a powerful optimization algorithm for solving the optimization problem. This paper proposes an improved pareto multi-objective backtracking search algorithm (IPMBSA) based Pareto front approach for optimal siting and sizing of DGs and AVRs. The proposed approach considers DS technical (such as minimizing of voltage deviation, power losses and voltage stability index), cost and emission indices as objective functions while solving the allocation problem. To evaluate the performance of the proposed approach, five different operational scenarios are considered for analysis on IEEE 33-bus, IEEE 69-bus and practical 43-bus Indian distribution test systems. The effectiveness of the proposed approach is highlighted by comparison of results obtained using a multi-objective approach based on NSGA-II and weighted sum method (WSM). Results propounded that allocation of DG along with AVR yielded better DS performance compared to the scenarios where only DG or AVRs are placed in DS.

A Conflict-Based Search Framework for Multiobjective Multiagent Path Finding

Conventional multi-agent path planners typically compute an ensemble of paths while optimizing a single objective, such as path length. However, many applications may require multiple objectives, say fuel consumption and completion time, to be simultaneously optimized during planning and these criteria may not be readily compared and sometimes lie in competition with each other. The goal of the problem is thus to find a Pareto-optimal set of solutions instead of a single optimal solution. Naively applying existing multi-objective search algorithms, such as multi-objective A* (MOA*), to multi-agent path finding may prove to be inefficient as the dimensionality of the search space grows exponentially with the number of agents. This article presents an approach named Multi-Objective Conflict-Based Search (MO-CBS) that attempts to address this so-called curse of dimensionality by leveraging prior Conflict-Based Search (CBS), a well-known algorithm for single-objective multi-agent path finding, and principles of dominance from multi-objective optimization literature. We also develop several variants of MO-CBS to improve its performance. We prove that MO-CBS and its variants can compute the entire Pareto-optimal set. Numerical results show that MO-CBS outperforms MOM*, a recently developed state-of-the-art multi-objective multi-agent planner.

Personalized Route Recommendation with Hybrid Tabu Search Algorithm Based on Crowdsensing

In the postmodern era of tourism, tourists’ behavior has undergone a substantial change and the demand of customized experience dominates the tourism market. The traditional single-objective travel route recommendation method fails to meet the multiobjective needs of users. To handle this problem, a multiobjective hybrid tabu search algorithm for urban travel route recommendations is proposed in this paper. First, the rating and level of attractions, as well as the corresponding information of hotels and restaurants within a certain radius, are considered. Then, based on this information, a crowdsensing scoring method is established. Second, the hybrid particle swarm genetic optimization algorithm is exploited to generate a single-object route, and the fast nondominated Pareto sorting algorithm is exploited to find the optimized solution. Then, the hybrid tabu algorithm is used to optimize the personalized route chosen according to multiple objects set by users. This algorithm combines the global search ability of the genetic algorithm and the neighborhood search ability of the tabu algorithm to prevent convergence from falling into a local optimum. Finally, the experiments are conducted on the real-world data collected from the Dianping and Ctrip web sites. The comparison with baseline algorithms indicates that the algorithm proposed in this paper provides accurate and reasonable route recommendations for users.

Hybrid Strategy to Improve the High-Dimensional Multi-Target Sparrow Search Algorithm and Its Application

This research combines the improved reference point selection strategy and the sparrow search algorithm with an enhanced competition mechanism to create a high-dimensional multi-objective sparrow search algorithm with an incorporated improved reference point selection strategy. First, the reference point selection approach is utilized to establish the reference points and sparrow populations, and the most important reference points are dynamically chosen to increase the global search ability. Then, the size of the search population and the method of searcher position updating are dynamically adjusted according to the size of the entropy difference between two adjacent generations of the population. Following, the convergence speed is increased by improving the follower position formula and extending the competition mechanism to high-dimensional multi-objective optimization. The Corsi variation operator improves the algorithm’s capacity to break out of its local optimum. Finally, we have used 12 standard benchmark test functions to evaluate the MaOISSA (Many/Multi-Objective Sparrow Search Algorithm based on Improved reference points) and compared it with many high-dimensional multi-objective algorithms. There were nine with substantial IGD values and eight with significant HV values. The findings revealed that MaOISSA had convergence and variety. The simulated results of the performance model for the defense science and technology innovation ecosystem demonstrate that MaOISSA offers a superior solution for tackling the high-dimensional, multi-objective issue, demonstrating the method’s efficacy.

Multi-Objective Cluster Head-Based Energy-Aware Routing Protocol using Hybrid Woodpecker and Flamingo Search Optimization Algorithm for Internet of Things Environment

Internet of Things (IoT) plays a vital role in the world of Internet and integrates computing devices. The digital machines provide unique identifiers. The capacity to send data through the network using a better path is one of the capabilities of digital machines. IoT generates huge data based on fast aggregation data and processes these data efficiently. In this paper, a multi-objective hybrid woodpecker and flamingo search optimization algorithm is proposed for finding optimum Cluster Head (CH)-based energy-aware routing protocol in IoTs environment (Hyb-WFSOA-CHS-IoT). Here, the proposed Hyb-WFSOA-CHS-IoT model executes routing method through CH. The woodpecker and flamingo search optimization algorithm is used as the intelligent CH selection that consumes same energy as sensors. Hybrid woodpecker and flamingo search optimization algorithm (Hyb-WFSOA) is examined by using fitness functions, like distance, delay, energy consumption, throughput. The proposed Hyb-WFSOA-CHS-IoT method is activated in MATLAB software. Then, the performance of the proposed system is examined with different metrics, like delay, packet delivery ratio, throughput, network lifetime, energy consumption. Therefore, the proposed method attains lower delay 28.38%, 32.34% and 47.45%, higher delivery ratio 19.34%, 23.12% and 18.96% and lower energy consumption 11.25%, 7.90% and 12.88% compared with existing methods, like multi-objective fractional gravitational search algorithm for energy-efficient routing on IoT (FGSA-CHS-IoT), multi-objective sunflower-based gray wolf optimization algorithm for multiple path routing on IoT network (SFG-CHS-IoT) and energy-aware routing in the IoT with improved grasshopper metaheuristic algorithm with chaos theory and fuzzy Logic (FLGOA-CHS-IoT).

Experimental validation of an AI-embedded FPGA-based Real-Time smart energy management system using Multi-Objective Reptile search algorithm and gorilla troops optimizer

This paper proposes an AI-embedded FPGA-based Smart Energy Management System (SEMS) that ensures intelligent, secure, consistent, and synchronous energy management in an isolated microgrid. The proposed techno-economic SEMS comprises two levels of control to achieve optimal management and operation for an isolated microgrid. The first level adopts the use of the FPGA as a central controller, which is characterized by its high processing speed and small settling time. The second level aims to develop a coordinated operation strategy based on the optimal operation and management of an isolated microgrid in order to optimize the coordinated use of backup sources. An efficient multi-objective optimization problem for optimal operation and management of the microgrid is formulated. Two multi-objective optimization algorithms namely, Gorilla Troops Optimizer (GTO) and Reptile Search Algorithm (RSA) are applied to solve the optimization problem. The three main objectives considered in this study are to minimize the operating costs, the loss of power supply probability (LPSP), and the surplus power consumed by the dummy load. The results prove the superiority of the RSA algorithm in achieving the goals of the objective functions. Within 100 min of the experimental testing, it achieves the lowest operating cost 166.2423 $. The cost savings reach about 6.467 % when using the RSA, while it is 6.0363 % when using the GTO. The developed SEMS reduces the wasted power in the dummy load. In addition, it achieves the lowest value of LPSP about zero, which is considered the best value as it ensures continuous supply.

Some Seeds Are Strong: Seeding Strategies for Search-based Test Case Selection

The time it takes software systems to be tested is usually long. Search-based test selection has been a widely investigated technique to optimize the testing process. In this article, we propose a set of seeding strategies for the test case selection problem that generates the initial population of Pareto-based multi-objective algorithms, with the goals of (1) helping to find an overall better set of solutions and (2) enhancing the convergence of the algorithms. The seeding strategies were integrated with four state-of-the-art multi-objective search algorithms and applied into two contexts where regression-testing is paramount: (1) Simulation-based testing of Cyber-physical Systems and (2) Continuous Integration. For the first context, we evaluated our approach by using six fitness function combinations and six independent case studies, whereas in the second context, we derived a total of six fitness function combinations and employed four case studies. Our evaluation suggests that some of the proposed seeding strategies are indeed helpful for solving the multi-objective test case selection problem. Specifically, the proposed seeding strategies provided a higher convergence of the algorithms towards optimal solutions in 96% of the studied scenarios and an overall cost-effectiveness with a standard search budget in 85% of the studied scenarios.

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.

A wind speed forecasting model based on multi-objective algorithm and interpretability learning

More accurate and reliable wind speed forecasting results can provide an effective assessment of wind energy resources and improve the efficiency of wind energy utilization. Therefore, this paper is devoted to constructing accurate wind speed forecasting models and quantitatively analyzing the models by using interpretable analysis. Firstly, a multivariate wind speed forecasting model (PMI-CMOGSA-RELM) is proposed based on machine learning methods and a clustering-based multi-objective gravity search algorithm (CMOGSA). Among them, wavelet packet decomposition (WPD) is used for noise reduction, candidate input feature pool and partial mutual information (PMI) are used for feature selection, and CMOGSA is used to optimize the final combined weights. Secondly, a new evaluation metric, Absolute Error Coverage Probability (AECP), is proposed to better evaluate forecasting accuracy. Then, post-hoc attribution analysis methods and visualization tools are used to analyze the forecasting model interpretability to help us better analyze the robustness and reliability of the forecasted results. Finally, this paper validates and evaluates the proposed model with two data sets of different resolutions. The experimental results not only prove the rationality of the AECP evaluation criteria, but also demonstrate that the proposed model has smaller errors, higher estimation accuracy, and is easier to understand.

Multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty of renewable energy generations based on two-level iterative strategy

In this study, multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty in renewable energy generations is designed to obtain optimal energy point sets of microgrid. The proposed dispatching model contains upper-level model and lower-level model. The economic and environmental benefits of the microgrid are adopted as the optimization objectives in the upper-level model and the economic benefits under incentive-based demand response is taken as the optimization objective in the lower-level load demand. In the upper-level model, sequence operation theory is designed to deal with the uncertainty in renewable energy generations, and virtual real-time tariffs and electricity credibility of power consumption are utilized to make demand response by shifting load in the lower-level model. To optimally solve the proposed model, improved enhanced multi-objective optimization sparrow search algorithm (EMOSSA) based on sinusoidal search strategy, diversity variant processing by Cauchy variation and external archive updating mechanism is developed to optimize the objective functions of the upper-level model and mixed integer linear programming CPLEX is employed to determine the load demand for the lower-level model. After that, the power demand plan of the lower-level model and the real-time prices of the upper-level model are updated iteratively to obtain the optimal dispatching solutions using the real-time pricing mechanism. Eventually, experimental results of several operation scenarios illustrate the proposed optimal dispatching method can deal with the uncertainty of renewable energy generations and the randomness of load, thus, achieving a well supply and demand balance between microgrid and users.

Picker routing optimization of storage stacker based on improved multi-objective iterative local search algorithm

<p style='text-indent:20px;'>In the process of warehouse automation, the efficient picker routing strategy is one of the key factors to improve warehouse efficiency. According to the order and the picking characteristics of the warehouse stacker, the picker routing of the stacker is regarded as the prize-collecting vehicle routing problem (PCVRP). According to the path and order for stacker picking, we designed a picker routing optimization strategy based on improved multi-objective iterative local search (IMOILS) algorithm. An elite pool strategy was set to save the high-quality solutions in the iterative process, and the nondominated solutions in the iterative evolution were stored and updated. Adding the adaptive enhanced perturbation strategy was beneficial to the current solution to escape from local optimum. In the local search procedure, to limit the excessively large search space, the epsilon constraint was adopted to limit the significant deterioration in another objective direction. The computational results showed that the proposed algorithm is superior to the comparison algorithms in the number of nondominated solutions, the hypervolume, and the spacing metric, and the stability of the algorithm is also good, which not only verifies the effectiveness of the proposed algorithm, but also provides a feasible optimization scheme for the storage stacker's picker routing problem.</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.

IPP: An Intelligent Privacy-Preserving Scheme for Detecting Interactions in Genome Association Studies.

Analyzing K-order Single Nucleotide Polymorphism (SNP) interactions through the statistics of Genome-Wide Association Studies (GWAS) is crucial for discovering pathogenic causes of human complex diseases and controlling risk genetic variants of diverse disorders. We propose a method based on Ant Colony Optimization (ACO) algorithm to detect gene interactions for GWAS - an Intelligent Privacy-Preserving scheme (IPP). Initially, we design a multi-objective search algorithm to discover the candidate SNP sets related to disease phenotype, which utilizes Differential Privacy (DP) method by disturbing the multi-objective function to construct a rational epistatic privacy protection strategy. Furthermore, the global path selection strategy composed of two probabilistic methods is proposed to reduce the probability of falling into the local optimum. We use simulated models and a real dataset of Rheumatoid Arthritis (RA) to compare IPP with four popular methods to detect K-order SNPs, the experimental results show that IPP can guarantee the search accuracy effectively and enhance the detecting ability of various models. Further, the privacy budget experiments indicate that the range of privacy budget in IPP is reasonable and make the framework more stable.

Modeling Optimal Energy Exchange Operation of Microgrids Considering Renewable Energy Resources, Risk-based Strategies, and Reliability Aspect Using Multi-objective Adolescent Identity Search Algorithm

The inherent volatility and unpredictable nature of renewable resources and load demand have posed significant challenges to the optimal energy exchange of microgrids (MGs). Therefore, this study presents a novel risk-based multi-objective energy exchange optimization for the networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. For this purpose, three different risk-based strategies are distinguished using the conditional value at risk approach. The proposed model is specified as an objective function in two distinct ways. The first function minimizes operation and maintenance costs, the cost of exchanging power between the upstream grid and MGs, and the cost of power loss, whereas the second function minimizes the amount of energy that is not supplied. Moreover, a stochastic scenario-based approach is incorporated into this approach to control uncertainties. Also, the scenario reduction method is implemented to reduce the computational burden. Finally, a developed multi-objective adolescent identity search algorithm (AISA) is proposed. Pareto theory and non-dominated sorting are employed to develop the proposed model. In the proposed data development model, it has been tried to improve the local and global search in a better way in order to solve local points and early convergence. The fuzzy model is used to select the optimal solution from the set of solutions. The modified IEEE 33-bus distribution system is used to implement the suggested model and assess its efficacy. The obtained results show that the proposed risk-based model can control the risk of the EMS problem due to the stochastic behavior of the input parameters. In this paper, for risk assessment high consequences and low probability events are considered extreme events. In the risk aversion strategy, the cost of the event is significantly reduced compared to the risk strategy, while the additional cost is much less than this is.