MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Optimization of LSTM Ship Trajectory Prediction Based on Hybrid Genetic Algorithm

Accurate prediction of the movement trajectory of sea surface targets holds significant importance in achieving an advantageous position in the sea battle field.This prediction plays a crucial role in ensuring security defense and confrontation,and is essential for effective deployment of military strategy.Accurately predicting the trajectory of sea surface targets using AIS(Automatic Identification System)information is crucial for security defense and confrontation,and holds significant importance for military strategy deployment.In response to the problem of insufficient accuracy in ship trajectory prediction,this study proposes a hybrid genetic algorithm to optimize the Long Short-Term Memory(LSTM)algorithm.The HGA-LSTM algorithm is proposed for ship trajectory prediction.It can converge faster and obtain better parameter solutions,thereby improving the effectiveness of ship trajectory prediction.Compared to traditional LSTM and GA-LSTM algorithms,experimental results demonstrate that this algorithm outperforms them in both single-step and multi-step prediction.

Design of S-band photoinjector with high bunch charge and low emittance based on multi-objective genetic algorithm

High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light sources.The bunch charge and RMS bunch length at the exit of the LINAC play a crucial role in the peak current;the minimum transverse emittance is mainly determined by the injector of the LINAC.Thus,a photoin-jector with a high bunch charge and low emittance that can simultaneously provide high-quality beams for 4th generation synchrotron radiation sources and FELs is desirable.The design of a 1.6-cell S-band 2998-MHz RF gun and beam dynamics optimization of a relevant beamline are presented in this paper.Beam dynamics simulations were performed by combining ASTRA and the multi-objective genetic algorithm NSGA II.The effects of the laser pulse shape,half-cell length of the RF gun,and RF parameters on the output beam quality were analyzed and compared.The normalized transverse emittance was optimized to be as low as 0.65 and 0.92 mm·mrad when the bunch charge was as high as 1 and 2 nC,respectively.Finally,the beam stability properties of the photoinjector,considering misalignment and RF jitter,were simulated and analyzed.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.

INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING

In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.

Optimization of Multi-Execution Modes and Multi-Resource-Constrained Offshore Equipment Project Scheduling Based on a Hybrid Genetic Algorithm

Offshore engineering construction projects are large and complex,having the characteristics of multiple execution modes andmultiple resource constraints.Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems(RCPSPs).To solve RCPSP problems in offshore engineering construction more rapidly,a hybrid genetic algorithmwas established.To solve the defects of genetic algorithms,which easily fall into the local optimal solution,a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation.Then,an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions,reduce the computation time and avoid premature convergence.A calibrated function method was used to cater to the roulette rules,and appropriate rules for encoding,decoding and crossover/mutation were designed.Finally,a simple network was designed and validated using the case study of a real offshore project.The performance of the genetic algorithmand a simulated annealing algorithmwas compared to validate the feasibility and effectiveness of the approach.

Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades

The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines.

Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm

A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered for optimization.Several multi-objective optimization problems involving twoand three-objective functions were formulated and solved to fix optimal drilling variables.The important objectives are：（i） maximizing drilling depth,（ii） minimizing drilling time and （iii） minimizing drilling cost with fractional drill bit tooth wear as a constraint.Important time dependent decision variables are：（i） equivalent circulation mud density,（ii） drill bit rotation,（iii） weight on bit and （iv） Reynolds number function of circulating mud through drill bit nozzles.A set of non-dominated optimal Pareto frontier is obtained for the two-objective optimization problem whereas a non-dominated optimal Pareto surface is obtained for the three-objective optimization problem.Depending on the trade-offs involved,decision makers may select any point from the optimal Pareto frontier or optimal Pareto surface and hence corresponding values of the decision variables that may be selected for optimal drilling operation.For minimizing drilling time and drilling cost,the optimum values of the decision variables are needed to be kept at the higher values whereas the optimum values of decision variables are at the lower values for the maximization of drilling depth.

Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.

Multi-objective optimization of stamping forming process of head using Pareto-based genetic algorithm

To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.

Hydraulic Optimization of a Double-channel Pump's Impeller Based on Multi-objective Genetic Algorithm

Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.

Satellite Constellation Design with Multi-Objective Genetic Algorithm for Regional Terrestrial Satellite Network

Constellations design for regional terrestrial-satellite network can strengthen the coverage for incomplete terrestrial cellular network. In this paper, a regional satellite constellation design scheme with multiple feature points and multiple optimization indicators is proposed by comprehensively considering multi-objective optimization and genetic algorithm, and "the Belt and Road" model is presented in the way of dividing over 70 nations into three regular target areas. Following this, we formulate the optimization model and devise a multi-objective genetic algorithm suited for the regional area with the coverage rate under simulating, computing and determining. Meanwhile, the total number of satellites in the constellation is reduced by calculating the ratio of actual coverage of a single-orbit constellation and the area of targets. Moreover, the constellations' performances of the proposed scheme are investigated with the connection of C++ and Satellite Tool Kit(STK). Simulation results show that the designed satellite constellations can achieve a good coverage of the target areas.

A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation

A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach.

Multi-objective optimization of membrane structures based on Pareto Genetic Algorithm

A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization variables,which are decision factors of shapes of membrane structures.Three objectives are proposed including maximization of stiffness,maximum uniformity of stress and minimum reaction under external loads.Pareto Multi-objective Genetic Algorithm is introduced to solve the Pareto solutions.Consequently,the dependence of the optimality upon the optimization variables is derived to provide guidelines on how to determine design parameters.Moreover,several examples illustrate the proposed methods and applications.The study shows that the multi-objective optimization method in this paper is feasible and efficient for membrane structures;the research on Pareto solutions can provide explicit and useful guidelines for shape design of membrane structures.

Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-II)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves.Then,an INSGA-II,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize travelling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.

Performance optimization of electric power steering based on multi-objective genetic algorithm

The vehicle model of the recirculating ball-type electric power steering (EPS) system for the pure electric bus was built. According to the features of constrained optimization for multi-variable function, a multi-objective genetic algorithm (GA) was designed. Based on the model of system, the quantitative formula of the road feel, sensitivity, and operation stability of the steering were induced. Considering the road feel and sensitivity of steering as optimization objectives, and the operation stability of steering as constraint, the multi-objective GA was proposed and the system parameters were optimized. The simulation results show that the system optimized by multi-objective genetic algorithm has better road feel, steering sensibility and steering stability. The energy of steering road feel after optimization is 1.44 times larger than the one before optimization, and the energy of portability after optimization is 0.4 times larger than the one before optimization. The ground test was conducted in order to verify the feasibility of simulation results, and it is shown that the pure electric bus equipped with the recirculating ball-type EPS system can provide better road feel and better steering portability for the drivers, thus the optimization methods can provide a theoretical basis for the design and optimization of the recirculating ball-type EPS system.

A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem

Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem （ MSPP ） has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.

Multi-objective genetic algorithm for the optimization of road surface cleaning process

The parameters affecting road surface cleaning using waterjets were researched and a fuzzy neural network method of calculating cleaning rate was provided. A genetic algorithm was used to configure the cleaning parameters of pressure, standoff distance, traverse rate and angle of nozzles for the optimization of the cleaning effectiveness, efficiency, energy and water con-sumption, and a multi-objective optimization model was established. After calculation, the optimized results and the trend of variation of cleaning effectiveness, efficiency, energy and water consumption in different weighting factors were analyzed.

Multi-objective Collaborative Optimization for Scheduling Aircraft Landing on Closely Spaced Parallel Runways Based on Genetic Algorithms

A scheduling model of closely spaced parallel runways for arrival aircraft was proposed,with multi-objections of the minimum flight delay cost,the maximum airport capacity,the minimum workload of air traffic controller and the maximum fairness of airlines′scheduling.The time interval between two runways and changes of aircraft landing order were taken as the constraints.Genetic algorithm was used to solve the model,and the model constrained unit delay cost of the aircraft with multiple flight tasks to reduce its delay influence range.Each objective function value or the fitness of particle unsatisfied the constrain condition would be punished.Finally,one domestic airport hub was introduced to verify the algorithm and the model.The results showed that the genetic algorithm presented strong convergence and timeliness for solving constraint multi-objective aircraft landing problem on closely spaced parallel runways,and the optimization results were better than that of actual scheduling.