Strength Pareto Evolutionary Algorithm Research Articles

Performance prediction and multi-objective optimization of metal seals in roller cone bits

Metal seals are the critical component of roller cone bits in oil and gas drilling. The performance of metal seals directly impacts the service life and efficiency of the entire drilling system. How to accurately predict the sealing performance of metal seals with multi-field coupling and optimize the overall sealing performance under the influence of various factors are the main difficulties of this study. A novel thermal-fluid-solid coupling numerical model was established and verified for the new-generation single energizer metal seals (SEMS2). The fluid film characteristics, interface temperatures, and sealing performance of SEMS2 were investigated under different operating conditions, structural parameters, and material parameters by the co-simulation of MATLAB and ANSYS. Based on the coupling analysis results, the significance analysis of the parameters was performed by using the orthogonal test. Furthermore, the multi-objective optimization of SEMS2 was carried out using the Back Propagation Neural Network (BP NN) and three typical multi-objective evolutionary algorithms (MOEAs). By comparison, the elitist non-dominated sorting genetic algorithm (NSGA-II) performed better than the strength Pareto evolutionary algorithm (SPEA2) and region-based Pareto Envelope based Selection Algorithm (PESA-II). The leakage rate and frictional force of SEMS2 were reduced by 52.3 % and 11.0 %, respectively, after multi-objective optimization. The results could provide theoretical support for designing a high-efficiency and long-life sealing system for onshore and offshore drill bits.

Economic Dispatch Methods for Smart Grid Based on Improved SPEA2 and Improved NSGA2

The severity of the ongoing environmental crisis has prompted the development of renewable energy generation and smart grids integration. The access of enewable energy makes the economic dispatching of smart grid complicated. Therefore, the economic dispatching model for smart grid is very necessary. This paper presents an economic dispatching model of smart power grid, which considers both economy and pollution emission. The smart grid model used for the simulation is construced of wind energy, solar energy, fuel cell, and thermal power, and the use of fuel cell enables the smart grid to achieve multi-energy complementar. To overcome the defect of the traditional centralized communication methods, which are prone to communication jams, this paper adopts a multi-agent inform ation exchange method to improve the stability and efficiency. In terms of the solution method for this model, this paper proposes Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2) and Improved Non-dominated Sorting Genetic Algorithm 2(INSGA2) that solves the economic dispatch problem of a smart grid. The strength Pareto evolutionary algorithm 2(SPEA2),non-dominated sorting genetic algorithm 2(NSGA2) and the improved algorithms are simultaneously applied to the proposed smart grid model for economic dispatching simulation. The simulation results show that ISPEA2 and INSGA2 are effective. ISPEA2 and INSGA2 have shown improvements over SPEA2 and NSGA2 in accuracy or running times.

Task Allocation on Layered Multiagent Systems: When Evolutionary Many-Objective Optimization Meets Deep Q-Learning

This article is concerned with the multitask multiagent allocation problem via many-objective optimization for multiagent systems (MASs). First, a novel layered MAS model is constructed to address the multitask multiagent allocation problem that includes both the original task simplification and the many-objective allocation. In the first layer of the model, the deep Q-learning method is introduced to simplify the prioritization of the original task set. In the second layer of the model, the modified shift-based density estimation (MSDE) method is put forward to improve the conventional strength Pareto evolutionary algorithm 2 (SPEA2) in order to achieve many-objective optimization on task assignments. Then, an MSDE-SPEA2-based method is proposed to tackle the many-objective optimization problem with objectives including task allocation, makespan, agent satisfaction, resource utilization, task completion, and task waiting time. As compared with the existing allocation methods, the developed method in this article exhibits an outstanding feature that the task assignment and the task scheduling are carried out simultaneously. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the effectiveness of two main algorithms and 2) illustrate the optimal solution for task allocation and efficient strategy for task scheduling under different scenarios.

An efficient slime mould algorithm for solving multi-objective optimization problems

Recently, the Slime mould algorithm (SMA) was proposed to solve the single-objective optimization problems. It is considered as a strong algorithm for its efficient global search capability. This paper presents a multi-objective optimization algorithm based on the SMA called multi-objective SMA (MOSMA). An external archive is utilized with the SMA to store the Pareto optimal solutions obtained. The archive applied to emulate the social behaviour of the slime mould in the multi-objective search space. The performance of the MOSMA is validated on the CEC’20 multi-objective benchmark test functions. Furthermore eight well-known of constrained and unconstrained test cases, four constrained engineering design problems are tested to demonstrate the MOSMA superiority. Moreover, the real-world multi-objective optimization of helical coil spring for automotive application to depict the reliability of the presented MOSMA to solve real-world problems. Over the statistical side, the Wilcoxon test and performance indicators are used to assess the effectiveness of MOSMA against six well-known and robust optimization algorithms: multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimization (MOPSO), multi-objective salp swarm algorithm (MSSA), Non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective whale optimization algorithm (MOWOA) and strength Pareto evolutionary algorithm 2 (SPEA2). The overall simulation results reveal that the proposed MOSMA has the ability to provide better solutions as compared to the other algorithms in terms of Pareto sets proximity (PSP) and inverted generational distance in decision space (IGDX) indicators.

Stochastic modelling of gravity data for 2D basement reliefs, free of regularization coefficient

In this paper, a modified version of strength Pareto evolutionary algorithm SPEA (II) is used as a multi-objective optimization method in gravity data modelling. In this method, a two-dimensional gravity inversion problem is solved by iteratively random creation of forward models. It is shown that it can be used as a fast and effective inversion tool in the depth-modelling of two-dimensional layer problems with applications in depth-to-basements, Geometry of bedrocks and sedimentary basins modelling cases. As a result of direct use of the regularization term as a separate objective function, smooth models have a high chance of being selected as final solutions, which makes the results more acceptable and easier to interpret. The most important advantages of this method are that it works independently of the regularization coefficient; so there is no need to run the algorithm so many times to find a proper regularization parameter. Furthermore, there is no need to directly deal with inverse formulations. and the last but not the least, by using a multi-objective algorithm as a global optimization method, convergence to a stable solution does not depend on the initial model, the way classical inversion methods do. For testing the algorithm, a synthetic model is used for layer boundary modelling. To assess the stability of this algorithm, white Gaussian noise added to the synthetic model, Finally, to evaluate the validity of this method, real data from the Reconcavo basin in Brazil considered for processing and inversion, and the results are compared to the ones from previous studies. All computations have been done in the GNU Octave 5.1.0 environment.

Enhancing energo-exergo-economic performance of Kalina cycle for low- to high-grade waste heat recovery: Design and optimization through deep learning methods

The Kalina cycle has proven to be a reliable bottoming cycle for low-grade waste heat recovery. However, compared with other recovery cycles (e.g. the Rankine cycle), it is characterized by a lower efficiency rate and constraints on the heating medium inlet temperature. This study proposes a systematic method for configuring and optimizing three novel Kalina-trilateral-based systems to overcome those disadvantages. This accurate technique integrates thermodynamics with deep learning to accelerate the computation process. First, the actual thermodynamic-economic features of the alternative systems are modeled through the analyses of energy, exergy, and economy. Second, the surrogate models of the systems are developed through a long short-term memory (LSTM) network. Third, the direct and hybrid optimization algorithms are applied separately to the actual and surrogate models. The objective functions yield thermal efficiency, exergy efficiency, and distributed payback time. Moreover, the strength Pareto evolutionary algorithm (SPEA-II) is employed to solve the multi-objective optimization problem. According to the results, the LSTM network had considerable power to simulate and predict the energo-exergo-economic performance of an energy system. Furthermore, the computation time was much shorter for a hybrid algorithm with the maintained accuracy. Consequently, the heating source temperature constraint was eliminated in the final alternative cycle (KTS-36). Compared with the base system (KCS-34), the thermodynamic and economic objective functions were improved by 74.3% and 34%, respectively.

Integrated Operation of Multi-Reservoir and Many-Objective System Using Fuzzified Hedging Rule and Strength Pareto Evolutionary Optimization Algorithm (SPEA2)

In this paper, a many-objective optimization algorithm was developed using SPEA2 for a system of four reservoirs in the Karun basin, including hydropower, municipal and industrial, agricultural, and environmental objectives. For this purpose, using 53 years of available data, hedging rules were developed in two modes: with and without applying fuzzy logic. SPEA2 was used to optimize hedging coefficients using the first 43 years of data and the last 10 years of data were used to test the optimized rule curves. The results were compared with those of non-hedging methods, including the standard operating procedures (SOP) and water evaluation and planning (WEAP) model. The results indicate that the combination of fuzzy logic and hedging rules in a many-objectives system is more efficient than the discrete hedging rule alone. For instance, the reliability of the hydropower requirement in the fuzzified discrete hedging method in a drought scenario was found to be 0.68, which is substantially higher than the 0.52 from the discrete hedging method. Moreover, reduction of the maximum monthly shortage is another advantage of this rule. Fuzzy logic reduced 118 million cubic meters (MCM) of deficit in the Karun-3 reservoir alone. Moreover, as expected, the non-hedging SOP and WEAP model produced higher reliabilities, lower average storages, and less water losses through spills.

Multi-objective Optimization of Vehicle Routing with Resource Repositioning

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

A New Multiobjective Time-Cost Trade-Off for Scheduling Maintenance Problem in a Series-Parallel System

In this paper, a modified model of Nondominated Sorting Genetic Algorithm 2 (NSGA-II), which is one of the Multiobjective Evolutionary Algorithms, is proposed. This algorithm is a new model designed to make a trade-off between minimizing the cost of preventive maintenance (PM) and minimizing the time taken to perform this maintenance for a series-parallel system. In this model, the limitations of labor and equipment of the maintenance team and the effects of maintenance issues on manufacturing problems are also considered. In the mathematical model, finding the appropriate objective functions for the maintenance scheduling problem requires all maintenance costs and failure rates to be integrated. Additionally, the effects of production interruption during preventive maintenance are added to objective functions. Furthermore, to make a better performance compared with a regular NSGA-II algorithm, we proposed a modified algorithm with a repository to keep more unacceptable solutions. These solutions can be modified and changed with the proposed mutation algorithm to acceptable solutions. In this algorithm, modified operators, such as simulated binary crossover and polynomial mutation, will improve the algorithm to generate convergence and uniformly distributed solutions with more diverse solutions. Finally, by comparing the experimental solutions with the solutions of two Strength Pareto Evolutionary Algorithm 2 (SPEA2) and regular NSGA-II, MNSGA-II generates more efficient and uniform solutions than the other two algorithms.

New hybrid approach HNSGA-III&SPEA/R: Application to optimization of powertrain mount system stiffness parameters

In this study, a new methodology, hybrid NSGA-III with SPEA/R (HNSGA-III&SPEA/R), has been developed to design and achieve cost optimization of powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the powertrain mount system. A hybrid HNSGA-III&SPEA/R is proposed with the integration of Strength Pareto evolutionary algorithm based on reference direction for Multi-objective (SPEA/R) and Many-objective optimization genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&SPEA/R is more efficient than the typical SPEA/R, NSGA-III. Powertrain mount system stiffness parameters optimization with HNSGA-III&SPEA/R is simulated respectively. It proved the potential of the HNSGA-III&SPEA/R for powertrain mount system stiffness parameter optimization problem.

Improved strength Pareto evolutionary algorithm based on reference direction and coordinated selection strategy

Improved strength Pareto evolutionary algorithm based on reference direction and coordinated selection strategy

Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)

Swarm-based nature-inspired optimization techniques are developing rapidly due to their universal acceptance and capability in solving various real-life challenges efficiently. Hybridization of swarm intelligence based optimization algorithms with multi-objective based solution techniques is creating a wide door in the field of optimization. The main focus of this work is, developing a hybrid Non-dominated Sorting Whale Optimization Algorithm (NSWOA), where swarm-based Whale Optimization Algorithm (WOA) is hybridized with multi-objective based, non-dominated sorting technique. This is done to develop an algorithm with efficient optima searching ability and faster computational speed. The application of NSWOA in optimizing the controller parameters of an islanded microgrid consisting of both static and dynamic load has been also described. SPSS software has been used to compare the performance of proposed NSWOA with non-dominated sorting genetic algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA) technique in optimizing the controller parameters of islanded microgrid model with multi-objective problem. It is obtained that, NSWOA requires an average of 4 iterations to reach the best optimum solution, which is less than other existing algorithms. Moreover, the computational time required by NSWOA is 2.9201 s, which proves that, it converges at a much faster rate compared to existing NSGA-II and SPEA algorithms.

A multi-objective evolutionary scheme for control points deployment in intelligent transportation systems

One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the non-dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength Pareto evolutionary algorithm -II (SPEA-II); and the Pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using Pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.

A multi-objective ring star vehicle routing problem for perishable items

This study proposes a multi-objective model of the capacitated vehicle routing problem to envisage delivering perishable items. The main objectives of the model are to minimize the quality degradation of the perishable items to be delivered, and delivery costs. Here we consider the m-ring star type of distribution network to match the real-world scenario perfectly. To establish a trade-off between the quality of the items and the delivery cost, we consider both the refrigerated and general vehicles. The customers are categorized into two types based on their position and the vehicle type by which the items are served. The demands of the cities are known in advance. It is also considered that the refrigerated vehicle’s capacity is homogeneous and higher than the capacity of the general type of vehicle. The proposed model is solved using a non-dominated sorting genetic algorithm and Strength Pareto evolutionary algorithm.

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool-indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices.

Multi-objective optimization of water distribution system: a hybrid evolutionary algorithm

The optimal operation of water distribution systems is complicated due to multiple objectives that are in conflict, such as water quality versus cost. This work proposes to combine Strength Pareto Evolutionary Algorithm (SPEAII) with Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, called MOPSO-SPEAII, to establish a multi-objective model which handles water quality, costs and storage-reliable requirement. The main idea is that genetic operators are combined with particle swarm operators such that the fitness of SPEAII results is evaluated using MOPSO. An optimization-simulation model is prepared by linking the hybrid algorithm with EPANET software, and it is employed for a typical case study from the literature. The model outcomes verify that the MOPSO-SPEAII is more stable compared to SPEAII in terms of closeness to global minimum and can be used as a robust decision tool. However, the model application for a real sized system increases the computational intensity of the model.

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method.

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

The quantitative techno-economic comparisons and multi-objective capacity optimization of wind-photovoltaic hybrid power system considering different energy storage technologies

To provide investors with a selection method of energy storage technology, this paper proposes a quantitative techno-economic comparison method of battery, thermal energy storage, pumped hydro storage and hydrogen storage in wind-photovoltaic hybrid power system from the perspective of multi-objective capacity optimization. The multi-objective capacity optimization models are developed based on minimizing the levelized cost of energy (economy) and loss of power supply probability (reliability) simultaneously. Comprehensive metrics based on hypervolume are proposed to compare the performance of four multi-objective evolutionary algorithms. Moreover, the operation characteristics of devices is considered in the model to improve the simulation accuracy. The performance comparisons of algorithms show that the average rank of nondominated sorting genetic algorithm, multi-objective evolutionary algorithm based on decomposition, multi-objective particle swarm optimization and strength Pareto evolutionary algorithm are 2.8, 3.6, 1.8 and 1.8 respectively, which demonstrates that multi-objective particle swarm optimization and strength Pareto evolutionary algorithm have relatively better overall performance when applied in capacity optimization problems. The quantitative techno-economic comparisons of energy storage show that the levelized cost of energy of thermal energy storage, battery, hydrogen storage and pumped hydro storage under the same reliability are 0.1224 $/kWh, 0.1812 $/kWh, 0.1863 $/kWh and 0.2225 $/kWh respectively, which demonstrates that thermal energy storage is the most cost-effective alternative. Furthermore, the sensibility analysis demonstrates that thermal energy storage is always the most cost-effective alternative for different load profile, different resources level and different energy storage cost. Finally, the conclusions can help investors to select a cost-effective and reliable energy storage technology.

Optimizing the metastability of high-strength ultrafine grained microstructure from large strain machining

Ultrafine-grained (UFG) microstructure of Cu processed by large strain machining (LSM) is explored in order to create highly refined grain structures to achieve the highest strength while postponing the available nuclei for future recrystallization. The optimum solution is obtained theoretically using the Strength Pareto Evolutionary Algorithm (SPEA) and empirically using LSM. The thermal stability of the optimal solution is verified across the comparable LSM conditions using isothermal annealing curve. We also studied the kinetics of crystallization on the optimal solution using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) theory. The optimal solution encountered leads to the latest time for the point where hardness start decline among a comparable sample conditions and lower the rate constant (1/τ) among LSM conditions.