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On the adaptation of reference sets using niching and pair-potential energy functions for multi-objective optimization

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SWARM AND evolutionary COMPUTATION 2023年 83卷

作者： Marquez-Vega, Luis A. Falcon-Cardona, Jesus Guillermo Osuna, Edgar Covantes Tecnol Monterrey Sch Engn & Sci Ave Eugenio Garza Sada 2501 Monterrey 64849 Nuevo Leon Mexico

multi-objective evolutionary algorithms (MOEAs) have shown a good capability to approximate the Pareto front (PF) of complex multi-objective optimization problems (MOPs). Recently, several decomposition-based, indicator-based, and reference set-based MOEAs have been proposed to tackle MOPs with four or more objective functions, known as many-objective optimization problems (MaOPs). Some well-known MOEAs from these design strategies use a predefined reference set generated with uniformly distributed weight vectors on a unit simplex. These MOEAs have demonstrated good convergence, coverage, and uniformity of solutions on MOPs with regular PF shapes, i.e., simplex-like shapes. However, it has been shown that their performance degrades on MOPs with irregular PF shapes. In this paper, we proposed a pluggable reference set adaptation method based on niching and pair-potential energy functions (PPFs), called Adak, to enhance the performance of these MOEAs on MOPs with irregular PF shapes while maintaining their good behavior on MOPs with regular PF shapes. Our adaptation method was validated by plugging it into three well-known MOEAs that use a predefined weight vector-based reference set. We perform an empirical study using different PPFs for our adaptation method on the DTLZ, WFG, Minus-DTLZ, Minus-WFG, IMOP, and VNT test suites. Our experimental results show the capability of our adaptation method to promote an invariant performance regardless of the PF shape. Additionally, we show that MOEAs with our adaptation method have a competitive performance against state-of-the-art MOEAs.

关键词： multi-objective evolutionary algorithms Reference sets Weight vectors Adaptation methods Pair-potential energy functions

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Enhancing soft computing techniques to actively address imbalanced regression problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期234卷

作者： Arteaga, Maria Gacto, Maria Jose Galende, Marta Alcala-Fdez, Jesus Alcala, Rafael Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Granada Dept Software Engn E-18071 Granada Spain CARTIF Ctr Tecnol Parque Tecnol Boecillo Valladolid 47151 Spain Univ Valladolid Sch Ind Engn Dept Syst Engn & Control Valladolid 47011 Spain

While research in the area of imbalance, which is understood as classes that are not equally represented, is mainly addressed in classification, it has hardly been studied in regression, where data maldistribution, or imbalance, can be defined as the existence of some specific subdomains of the output variable misrepresented in the training data set, resulting in low accuracy for new instances within these subdomains. The small amount of state-of-the-art techniques are "passive", meaning they are only applied in preprocessing. In this contribution, we propose two new specific evolutionary algorithms based on fuzzy rules to "actively"address imbalanced regression problems and improve the overall performance of the algorithms instead of just addressing the imbalance problem. The results obtained after applying statistical tests to 32 regression datasets that handle more than 3000 partitions show the effectiveness of the proposed methods when compared to the best previous proposal, a passive method called SMOGN. We can conclude: (1) we cannot affirm, since the equality hypotheses have not been rejected, that there are significant performance differences between using stratified and non-stratified data, thus we will use stratification to preserve a minimum representation of the minority set, (2) both fuzzy rule-based methods obtain better results in terms of the imbalance metric when using SMOGN, but in both methods this incurs a cost in accuracy (with confidence scores of over 99.0%), and (3) the proposed methods outperform those using SMOGN as they get slightly better results in the imbalance metric, with better average ranks in both proposals, and obtain significantly better results in global accuracy, that is, all the performance metrics studied improve statistically with a confidence score of over 99.0%, with the exception of one metric, which scores above 90.0%.

关键词： Imbalanced regression multi-objective evolutionary algorithms Fuzzy rule-based systems

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Proposal and Comparative Study of evolutionary algorithms for Optimum Design of a Gear System

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IEEE ACCESS 2020年 8卷 3482-3497页

作者： Mendez, Maximo Rossit, Daniel A. Gonzalez, Begona Frutos, Mariano Univ Las Palmas de Gran Canaria ULPGC Inst Univ SIANI Las Palmas Gran Canaria 35001 Spain Univ Nacl Sur Dept Engn RA-8000 Bahia Blanca Buenos Aires Argentina INMABB CONICET RA-8000 Bahia Blanca Buenos Aires Argentina IIESS CONICET RA-8002 Bahia Blanca Buenos Aires Argentina

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.

关键词： Differential evolution evolutionary computation gear train optimization genetic algorithms mechanical engineering multi-objective evolutionary algorithms non-dominated sorting genetic algorithm-II

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Handling performance optimisation for formula vehicle using multi-objectives evolutionary algorithms

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VEHICLE SYSTEM DYNAMICS 2020年第12期58卷 1823-1838页

作者： Tey, J. Y. Rahizar, R. Univ Tunku Abdul Rahman Dept Mech & Mat Engn Lee Kong Chian Fac Engn & Sci Selangor Malaysia Univ Malaya Dept Mech Engn Fac Engn Adv Computat & Appl Mech ACAM Res Grp Kuala Lumpur Malaysia

A method based on constraint multi-objective evolutionary algorithms approach is proposed to optimise the handling performance of a race car dynamic. This involves a software-in-the-loop optimisation (SiLO) between a high-fidelity model of a formula vehicle modelled in VI-Grade and MATLAB for hosting the optimisation algorithms. The optimisation process involves the tuning of the suspension design variables, i.e. spring stiffness, anti-roll bar stiffness, damper coefficient, toe angle, and camber angle for both front and rear suspensions against eight objectives functions, i.e. sideslip overshoot, yaw rate overshoot, linear understeer gradient, limit understeer gradient, maximum lateral acceleration, yaw rate phase delay and lateral acceleration phase delay. A custom constraint function was introduced to improve the convergence speed of the optimisation process. The optimised result showed a promising increase in handling performances, i.e. the maximum achievable lateral acceleration has increased by 8.9% whilst maintaining the stability of the vehicle close to neutral steer behaviour. The sideslip angle was well controlled under 1 deg at 95% of maximum lateral acceleration, and slip angle overshoot has improved by 1.65% in step steer manoeuvre. Additionally, the skid-pad events and autocross event laps time were reduced by 5.19% and 1.71%, respectively.

关键词： Handling performance multi-objective evolutionary algorithms NSGA-II CMOEA D-DE

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Cooperative coevolutionary multi-guide particle swarm optimization algorithm for large-scale multi-objective optimization problems

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SWARM AND evolutionary COMPUTATION 2023年 78卷

作者： Madani, Amirali Engelbrecht, Andries Ombuki-Berman, Beatrice Brock Univ Dept Comp Sci St Catharines ON Canada Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Stellenbosch Univ Comp Sci Div Stellenbosch South Africa

Many problems that are encountered in real-life applications consist of two or three conflicting objectives and many decision variables. multi-guide particle swarm optimization (MGPSO) is a novel meta-heuristic for multi -objective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other state-of-the-art multi-objective optimization algorithms for low-dimensional (and even many-objective) problems. However, a recent study has shown that MGPSO does not scale well when the number of decision variables is increased. This paper proposes a new scalable MGPSO-based algorithm, termed cooperative coevolutionary multi-guide particle swarm optimization (CCMGPSO), that incorporates ideas from cooperative coevolution (CC). CCMGPSO uses new techniques to spend less computational budget by periodically assigning only one CC-based subswarm to each objective (as opposed to using numerous CC -based subswarms). A detailed empirical study on well-known benchmark problems comparing the CCMGPSO with various state-of-the-art large-scale multi-objective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems with reference to the inverted generational distance (IGD) metric.

关键词： Particle swarm optimization multi-objective optimization multi-objective evolutionary algorithms Large-scale multi-objective optimization Cooperative coevolution multi-guide particle swarm optimization

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Unsupervised text feature selection using NSGA II with Hill Climbing local search

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Procedia Computer Science 2023年 225卷 1201-1210页

作者： Laura Cornei Eugen Croitoru Henri Luchian Faculty of Computer Science “Alexandru Ioan Cuza” University 16 General Berthelot St. Iasi 700483 Romania

This paper introduces a novel unsupervised text feature selection technique that combines the multi-objective evolutionary algorithm NSGA II with a local Hill Climbing based search. The objective functions in NSGA II are an adapted version of the Mean Absolute Difference criterion and the Holes projection pursuit index, both of which incorporate information regarding the cardinality of the selected feature subset. The local Hill Climbing procedure aims to further improve the solutions provided by NSGA II through elimination/replacement of redundant features. The redundancy level of a feature F in a chromosome is measured via the lexical and semantic similarities between F and the other features represented in the chromosome. Experiments conducted on three real-world textual subdatasets indicate that our proposed approach achieves very good results in comparison to four state-of-the-art evolutionary unsupervised feature selection techniques and to one classical feature extraction method with proven effectiveness (Principal Component Analysis).

关键词： unsupervised text feature selection multi-objective evolutionary algorithms NSGA-II Hill Climbing NLP semantic similarity

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RCS Reduction For UAV Array

RCS Reduction For UAV Array

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Cross Strait Radio Science and Wireless Technology Conference (CSRSWTC)

作者： Zhou, Lei Zhou, Haosu Wang, Yizhao Liu, Beichen Lv, Zhenbin Tang, Qi Shanghai Spaceflight Elect & Commun Equipment Res Sci & Technol Near Surface Detect Lab Shanghai Peoples R China Shanghai Spaceflight Elect & Commun Equipment Res Shanghai Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Sci & Technol Near Suiface Detect Lab Wuxi Jiangsu Peoples R China

ISBN: (纸本)9781665432436

It is difficult to achieve the effect of raid target because of the large Radar Cross Section (RCS) of Unmanned Aerial Vehicle (UAV) group when carrying out military missions. This paper proposes a method to reduce RCS of UAV linear array. Based on the principle of array scattering pattern synthesis, this paper calculates the RCS of a linear array UAV group and optimizes the posture of the UAV through a multi-objective evolutionary algorithm so that the RCS of the UAV group is significantly reduced within the viewing angle. The simulation results show that the reasonable arrangement of UAVs significantly reduces the RCS of the group within the specified viewing angle range.

关键词： scattering pattern function multi-objective evolutionary algorithms Radar Cross Section (RCS)

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Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

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Heliyon 2024年第20期10卷 e39041页

作者： Ali, Aamir Aslam, Sumbal Keerio, M.U. Mirsaeidi, Sohrab Mugheri, Noor Hussain Ismail, Muhammad Abbas, Ghulam Othmen, Salwa Department of Electrical Engineering Quaid-e-Awam University of Engineering Science and Technology Sindh Nawabshah 67450 Pakistan School of Electrical Engineering Beijing Jiaotong University Beijing 100044 China School of Electrical Engineering Southeast University Nanjing 210096 China Department of Computers and Information Technologies College of Sciences and Arts Turaif Northern Border University Arar 91431 Saudi Arabia

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. multi-objective evolutionary algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure

关键词： Economic dispatch Fixed reserve multi-objective evolutionary algorithms Renewable energy resources Voltage stability index

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Inconstant Update of Reference Point Value for Parallel and Distributed MOEA/D

Inconstant Update of Reference Point Value for Parallel and ...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Sato, Mikiko Sato, Yuji Midtlyng, Mads Miyakawa, Minami Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Hosei Univ Fac Comp & Informat Sci Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Shinshu Univ Fac Engn Nagano Japan

ISBN: (纸本)9781728183923

This paper proposes an update method to determine the reference point and nadir point when speeding up multi-objective evolutionary optimization algorithm based on decomposition (MOEA/D) with parallel and distributed processing in a many-core environment. We have already proposed a migration method that uses a virtual overlapping zone between partitions. Compared with simply partitioning the objective space or applying the standard island migration model, this method is shown to be effective in improving the hypervolume (HV) value, increasing diversity, and reducing regions where non-dominated solutions do not exist between partitions (sparse areas). However, this proposed method could not completely deal with the sparse areas' problem in the early stage of solution searching. To resolve this problem, in addition to the virtual overlap method, we propose here a method that switches the update interval for determining the reference point and nadir point by taking into account the progress of solution searching. To evaluate our newly proposed method, we compare both convergence and diversity on single-core MOEA/D and parallel MOEA/D using a two-objective constrained knapsack problem, and show that our method is effective not only in reducing sparse areas in the early stage but also in improving diversity and increasing the HV value.

关键词： MOEA/D parallel and distributed processing reference point multi-objective evolutionary algorithms

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Towards a More Balanced Reference Set Adaptation Method: First Results

Towards a More Balanced Reference Set Adaptation Method: Fir...

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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Marquez-Vega, Luis A. Guillermo Falcon-Cardona, Jesus Covantes Osuna, Edgar Tecnol Monterrey Sch Engn & Sci Monterrey Nuevo Leon Mexico UAM Cuajimalpa Dept Appl Math & Syst Mexico City DF Mexico

ISBN: (纸本)9781728183923

Reference sets are widely used by many multi-objective evolutionary algorithms (MOEAs) to decompose the objective space, define search directions, or calculate quality indicators (QIs) embedded into the selection mechanisms. Well-known MOEAs adopt the generation of uniformly distributed points on a unit simplex to construct such reference sets. Although these mechanisms are useful for approximating Pareto fronts with regular shapes, i.e., simplex-like shapes, they have difficulties representing Pareto fronts with irregular geometries. To overcome this drawback, many reference set adaptation methods have been proposed so far. However, some adaptation methods present a degraded performance on regular Pareto front shapes, while others promote a balanced performance. Nevertheless, an extensive assessment has not been made. In this paper, a MOEA based on the inverted generational distance plus indicator, using an adaptive reference set, is used to study the performance of well-known adaptation methods. Although an adaptation method promotes a balanced performance on both regular and irregular Pareto front shapes, results show some difficulties related to the distribution of solutions in complex Pareto front shapes. The results of this study allow detecting the main drawbacks of adaptation methods, which can be addressed by using diversity-oriented selection mechanisms in the generation of reference sets. Hence, these could impact the generation of reference set-based MOEAs achieving good coverage, convergence, and diversity regardless of the Pareto front shape.

关键词： multi-objective evolutionary algorithms reference set adaptation methods irregular Pareto front shapes

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