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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Reijnen, Robbert Zhang, Yingqian Bukhsh, Zaharah Guzek, Mateusz Eindhoven Univ Technol Dept Ind Engn Eindhoven Netherlands Goodyear Innovat Ctr Luxembourg Decis Sci & Optimizat Colmar Berg Luxembourg

ISBN: (纸本)9781665487689

Evolutionary algorithms (EA) are efficient population-based stochastic algorithms for solving optimization problems. The performance of EAs largely depends on the configuration of values of parameters that control their search. Previous works studied how to configure EAs, though, there is a lack of a general approach to effectively tune EAs. To fill this gap, this paper presents a consistent, automated approach for tuning and controlling parameterized search of an EA. For this, we propose a deep reinforcement learning (DRL) based approach called `DRL-APC-DE' for online controlling search parameter values for a multi-objective Differential Evolution algorithm. The proposed method is trained and evaluated on widely adopted multi-objective test problems. The experimental results show that the proposed approach performs competitively to a non-adaptive Differential Evolution algorithm, tuned by grid search on the same range of possible parameter values. Subsequently, the trained algorithms have been applied to unseen multi-objective problems for the adaptive control of parameters. Results show the successful ability of DRL-APC-DE to control parameters for solving these problems, which has the potential to significantly reduce the dependency on parameter tuning for the successful application of EAs.

关键词： Evolutionary Algorithms Deep Reinforcement Learning multi-objective optimization problems Adaptive Parameter Control Differential Evolution

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Fuzzy multi-objective Particle Swarm optimization Based on Linear Differential Decline 13th

Fuzzy Multi-objective Particle Swarm Optimization Based on L...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Li, Nana Yuan, Lian Shu, Xiaoli Han, Huayao Yang, Jie Liu, Yanmin Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Guizhou Peoples R China Zunyi Normal Coll Zunyi 563002 Guizhou Peoples R China Guizhou Xingqian Informat Technol Co Ltd Guiyang 563002 Guizhou Peoples R China

ISBN: (纸本)9783031096778;9783031096761

To improve convergence performance of the algorithm and prevent the algorithm from falling into local optimal location, we proposes a novel fuzzy multi-objective particle swarm optimization based on linear differential decline (LDDFMOPSO). In LDDFMOPSO, the fuzzy control strategy is applied to the inertia weight, so that the search ability of the global and local can be flexibly adjusted, thereby improving convergence performance of the algorithm. At the same time, in order to prevent the algorithm from falling into local optimal location, the strategy of linear differential decline is used to adjust the position change of particles. The experimental results illustrate that LDDFMOPSO has good performance compared to four state-of-the-art multi-objective particle swarm optimizations.

关键词： Particle swarm optimization multi-objective optimization problems Fuzzy control Linear differential decline

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A Direction based multi-objective Agent Genetic Algorithm

A Direction based Multi-Objective Agent Genetic Algorithm

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14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Zhu, Chen Liu, Jing Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9783642412783;9783642412776

A direction based multi-objective agent genetic algorithm (DMOAGA) is proposed in this paper. In order to take advantage of the effective direction information and depth of local search to mine non-dominated solutions, the direction perturbation operator is also employed. The neighborhood non-dominated solutions are generated using tournament selection and "average distance" rule, which maintains the diversity of non-dominated solution set. In the experiments, the benchmark problems UF1 similar to UF6 and ZDT1 similar to ZDT4 are used to validate the performance of DMOAGA. We compared it with NSGA-II and DMEA in terms of generational distance (GD) and inverted generational distance (IGD). The results show that DMOAGA has a good diversity and convergence, the performances on most of benchmark problems are better than DMEA and NSGA-II.

关键词： multi-objective optimization problems Direction information multi-agent systems Genetic algorithms

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Methodologies for Solving Complex multi-objective Combinatorial problems in Engineering: An Evolutionary Approach

Methodologies for Solving Complex Multi-Objective Combinator...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Donoso, Yezid Univ Los Andes Syst & Comp Engn Dept Master Program Informat Secur Bogota Colombia

ISBN: (纸本)9781509011476

In real problems in Engineering, solving a problem is not enough;the solution of the problem must be the best solution possible. In other words, it is necessary to find the optimal solution. The solution is the best possible solution because in the real world this problem may have certain constraints by which the solutions found may be feasible, that is, they can be implemented in practice and, unfeasible or that they cannot be implemented. Some of these problems in engineering can be MOP (multi-objective optimization Problem). A general MOP includes a set of n parameters ( decision variables), a set of k objective functions and a set of m restrictions. The objective and restriction functions are functions of the decision variables where is possible to obtain a set of optimal values. Then the MOP can be expressed as: Optimize y = f(x) = (fl( x), f2(x),..., fk(x)) Subject to e(x) = (el(x), e2(x),..., em(x)) 0 Where x = (x1, x2,..., xn) X y = (y1, y2,..., yk) Y The method evolutionary algorithm (EA) refers to searching and optimization techniques based on the evolution model proposed by Charles Darwin. Genetic algorithms are used in several areas especially for searching and optimizations. In the real case the algorithm is implemented by choosing a coding for the possible solutions to the problem. The coding is done through chains of bits, numbers or characters that represent the chromosomes. The crossing and mutation operations are applied in a very simple way through functions of vector value manipulation. The EAs are interesting given the fact that at first glance they seem especially apt to deal with the difficulties presented by MOPs. The reason for this is that they can return an entire set of solutions after a simple run and they do not have any other of the limitations of traditional techniques. In addition, some researchers have suggested that the EAs would behave better than other blind searching techniques.

关键词： Complex problems Evolutionary Algorithms multi-objective optimization problems multi-objective Evolutionary Algorithms

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An IGD⁺ Performance Indicator Based Particle Swarm Optimizer For multi-objective optimization 33

An IGD<SUP>+</SUP> Performance Indicator Based Particle Swar...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Li, Fei Dung, Shijian Liu, Yuanqu Shang, Zhengkun Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China Anhui Univ Technol AnHui Prov Key Lab Special Heavy Load Robot Maanshan 243002 Peoples R China Maanshan Univ Maanshan 243100 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China

ISBN: (纸本)9781665440899

Particle swarm optimizer (PSO) is suitable for solving multi-objective optimization problems (MOPs). However, there are two main issues for any multi-objective particle swarm optimizers (MOPSOs). The first issue is how to balance the convergence and diversity. The second issue is how to enhance the exploitation and exploration during the evolutionary procedure. In order to address these issues, an modified inverted generational distance (IGD(+)) performance indicator based PSO (IGD(+)-MOPSO) is proposed. The external archive updating strategies based on the IGD(+) indicator and the objective space decomposition method are proposed to select the evenly distributed non-dominated solutions. The leader updated strategy of each particle is based on the IGD(+) indicator value which is associated to the corresponding reference vector. The genetic operator is embedded into the evolutionary procedure to reset the position in order to help the particle jump out of the local optimum. We have conducted the simulation on some related benchmark test instances. The experimental results have indicated that the proposed algorithm is competitive with some related algorithms.

关键词： IGD(+) indicator Particle swarm optimization The external archive updated strategy objective space decomposition multi-objective optimization problems

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Lightweight multi-objective Voice Adaptation for Real-time Speech Interaction Applied in Games

Lightweight Multi-objective Voice Adaptation for Real-time S...

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IEEE Conference on Games (IEEE CoG)

作者： Midtlyng, Mads Sato, Yuji Hosei Univ Dept Comp Sci Tokyo Japan

ISBN: (纸本)9781728145334

This paper proposes a novel voice adaptation method that we applied to interactive activities such as games where source and target data are unaligned. Conventional methods have seen the use of probabilistic models or more recently, Deep Neural Networks. Common for most methods is that they require multiple subjects to train in conjunction, thus voice adaptation is not practical to be used in commercial applications. We propose a method which convert audible frequencies to light spectrum simple RGB color format, and not comparing sound signal similarities, but rather likeness in color. The comparison is done using multi-objective optimization which considers raw and normalized frame colors as two separate objectives to be evaluated, respectively audible and spectral structure. The distance for the objectives is used to select an ideal output frame. Finally, prosodic information such as speech intensity is translated from measured input values onto the designated output frame. The method is evaluated using MOS, ABX, performance benchmark and lastly implemented into the Unity3D game engine as a proof of concept. Results show good sound quality and high performance with little output fragmentation.

关键词： voice adaptation speech processing real-time multi-objective optimization problems video games

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A cluster prediction strategy with the induced mutation for multi

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INFORMATION SCIENCES 2024年 661卷

作者： Xu, Kangyu Xia, Yizhang Zou, Juan Hou, Zhanglu Yang, Shengxiang Hu, Yaru Liu, Yuan Minist Educ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Xiangtan Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan Hunan Peoples R China Xiangtan Univ Fac Sch Comp Sci Sch Cyberspace Sci Xiangtan 411105 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England

Dynamic multi -objective optimization problems (DMOPs) are multi -objective optimization problems in which at least one objective and/or related parameter vary over time. The challenge of solving DMOPs is to efficiently and accurately track the true Pareto-optimal set when the environment undergoes changes. However, many existing prediction -based methods overlook the distinct individual movement directions and the available information in the objective space, leading to biased predictions and misleading the subsequent search process. To address this issue, this paper proposes a prediction method called IMDMOEA, which relies on cluster center points and induced mutation. Specifically, employing linear prediction methods based on cluster center points in the decision space enables the algorithm to rapidly capture the population's evolutionary direction and distributional shape. Additionally, to enhance the algorithm's adaptability to significant environmental changes, the induced mutation strategy corrects the population's evolutionary direction by selecting promising individuals for mutation based on the predicted result of the Pareto front in the objective space. These two complementary strategies enable the algorithm to respond faster and more effectively to environmental changes. Finally, the proposed algorithm is evaluated using the JY, dMOP, FDA, and F test suites. The experimental results demonstrate that IMDMOEA competes favorably with other state-of-the-art algorithms.

关键词： Dynamic multi-objective optimization Evolutionary algorithms multi-objective optimization problems Prediction-based reaction

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Register-Pressure-Aware Instruction Scheduling Using Ant Colony optimization

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE optimization 2022年第2期19卷 23-23页

作者： Shobaki, Ghassan Gordon, Vahl Scott McHugh, Paul Dubois, Theodore Kerbow, Austin Calif State Univ Sacramento 6000 J St Sacramento CA 95819 USA

This paper describes a new approach to register-pressure-aware instruction scheduling, using Ant Colony optimization (ACO). ACO is a nature-inspired optimization technique that researchers have successfully applied to NP-hard sequencing problems like the Traveling Salesman Problem (TSP) and its derivatives. In this work, we describe an ACO algorithm for solving the long-standing compiler optimization problem of balancing Instruction-Level Parallelism (ILP) and Register Pressure (RP) in pre-allocation instruction scheduling. Three different cost functions are studied for estimating RP during instruction scheduling. The proposed ACO algorithm is implemented in the LLVM open-source compiler, and its performance is evaluated experimentally on three different machines with three different instruction-set architectures: Intel x86, ARM, and AMD GPU. The proposed ACO algorithm is compared to an exact Branch-and-Bound (B&B) algorithm proposed in previous work. On x86 and ARM, both algorithms are evaluated relative to LLVM's generic scheduler, while on the AMD GPU, the algorithms are evaluated relative to AMD's production scheduler. The experimental results show that using SPECrate 2017 Floating Point, the proposed algorithm gives geometric-mean improvements of 1.13% and 1.25% in execution speed on x86 and ARM, respectively, relative to the LLVM scheduler. Using PlaidML on an AMD GPU, it gives a geometric-mean improvement of 7.14% in execution speed relative to the AMD scheduler. The proposed ACO algorithm gives approximately the same execution-time results as the B&B algorithm, with each algorithm outperforming the other on a substantial number of hard scheduling regions. ACO gives better results than B&B on many large instances that B&B times out on. Both ACO and B&B outperform the LLVM algorithm on the CPU and the AMD algorithm on the GPU.

关键词： Compiler optimizations Ant Colony optimization instruction scheduling instruction scheduling for the GPU register-pressure reduction spill code minimization NP-complete problems multi-objective optimization problems

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Quadratic approximation-based hybrid Artificial Cooperative Search algorithm for economic emission load dispatch problems

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2017年第4期27卷 1-14页

作者： Turgut, Mert Sinan Demir, Guleser Kalayci Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Mechatron Engn TR-35390 Izmir Turkey Dokuz Eylul Univ Dept Elect & Elect Engn TR-35390 Izmir Turkey

In this article, Artificial Cooperative Search (ACS) algorithm is incorporated with the quadratic approximation (QA) operator to solve the multi-objective economic emission load dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence-based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, which is effective at global search;however, it does not perform so well at exploring promising regions. The QA operator, on the other hand, is a non-derivative-based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D-dimensional space. Solving the EELD problems with the hybridized ACS-QA algorithm, as being proposed in the present article, leads to more accurate results with fewer function evaluations. Also, multi-objectivity of the problem is handled by transforming it into a single-objective problem by using the weighted sum method. The efficiency of the proposed ACS-QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterward, the proposed ACS-QA algorithm and the ACS algorithm are implemented on multi-objective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS-QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls.

关键词： Artificial Cooperative Search algorithm economic emission load dispatch multi-objective optimization problems quadratic approximation operator

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Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Liu, Shili Yang, Fan Li, Jianqing Cheng, Zhiyu Zhang, Tianzhong Cheng, Tingli Guo, Yuanjun State Grid Anhui Elect Power Co Ltd Econ Res Inst Hefei Peoples R China State Grid Anhui Elect Power Co LTD Hefei Peoples R China Hefei Univ Technol Sch Elect & Automat Engn Hefei 230000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

关键词： distributed PV integration capacity voltage TLBO multi-objective optimization problems

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