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multi-objective immune algorithm with Baldwinian learning

APPLIED SOFT COMPUTING 2012年第8期12卷 2654-2674页

作者： Qi, Yutao Liu, Fang Liu, Meiyun Gong, Maoguo Jiao, Licheng Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators designed for SOPs might not be suitable for MOPs due to the different optima structures between them. At present, few works have been done to improve the searching efficiency of MOEAs according to the characteristic of MOPs. Based on the regularity of continues MOPs, a Baldwinian learning strategy is designed for improving the nondominated neighbor immune algorithm and a multi-objective immune algorithm with Baldwinian learning (MIAB) is proposed in this study. The Baldwinian learning strategy extracts the evolving environment of current population by building a probability distribution model and generates a predictive improving direction by combining the environment information and the evolving history of the parent individual. Experimental results based on ten representative benchmark problems indicate that, MIAB outperforms the original immune algorithm, it performs better or similarly the other two outstanding approached NSGAII and MOEA/D in solution quality on most of the eight testing MOPs. The efficiency of the proposed Baldwinian learning strategy has also been experimentally investigated in this work. (C) 2012 Elsevier B. V. All rights reserved.

关键词： multi-objective optimization problems Immune optimization algorithm Memetic algorithm Baldwinian learning

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An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第5期23卷 2017-2064页

作者： Nikkhoo, Fatemeh Kashan, Ali Husseinzadeh Ostadi, Bakhtiar Nikbakhsh, Ehsan Tarbiat Modares Univ Fac Ind & Syst Engn Tehran Iran

Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem's criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

关键词： Order picking system human factors multi-attribute decision-making multi-objective optimization problems non-dominated sorting genetic algorithm improved augmented epsilon constraint method

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Voice adaptation by color-encoded frame matching as a multi-objective optimization problem for future games

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COMPLEX & INTELLIGENT SYSTEMS 2022年第2期8卷 1539-1550页

作者： Midtlyng, Mads Sato, Yuji Hosobe, Hiroshi Hosei Univ Dept Comp Sci Tokyo Japan Hosei Univ Fac Comp & Informat Sci Tokyo Japan

Voice adaptation is an interactive speech processing technique that allows the speaker to transmit with a chosen target voice. We propose a novel method that is intended for dynamic scenarios, such as online video games, where the source speaker's and target speaker's data are nonaligned. This would yield massive improvements to immersion and experience by fully becoming a character, and address privacy concerns to protect against harassment by disguising the voice. With unaligned data, traditional methods, e.g., probabilistic models become inaccurate, while recent methods such as deep neural networks (DNN) require too substantial preparation work. Common methods require multiple subjects to be trained in parallel, which constraints practicality in productive environments. Our proposal trains a subject nonparallel into a voice profile used against any unknown source speaker. Prosodic data such as pitch, power and temporal structure are encoded into RGBA-colored frames used in a multi-objective optimization problem to adjust interrelated features based on color likeness. Finally, frames are smoothed and adjusted before output. The method was evaluated using Mean Opinion Score, ABX, MUSHRA, Single Ease Questions and performance benchmarks using two voice profiles of varying sizes and lastly discussion regarding game implementation. Results show improved adaptation quality, especially in a larger voice profile, and audience is positive about using such technology in future games.

关键词： Voice adaptation Speech processing Color-encoding multi-objective optimization problems Video games

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Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization

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International Journal of Intelligent Computing and Cybernetics 2018年第1期11卷 20-46页

作者： K.Shankar Akshay S.Baviskar Department of mechanical engineering indian institute of technology madraschennaiindia

Purpose–The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current *** proposed application is for engineering design ***/methodology/approach–This study proposes two novel approaches which focus on faster convergence to the Pareto front(PF)while adopting the advantages of Strength Pareto Evolutionary Algorithm-2(SPEA2)for better *** first method,decision variables corresponding to the optima of individual objective functions(Utopia Point)are strategically used to guide the search toward *** second method,boundary points of the PF are calculated and their decision variables are seeded to the initial ***–The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance *** evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms(such as NSGA-II and SPEA2)and recent ones such as NSLS and E-NSGA-II in most of the benchmark *** is also tested on an engineering design problem and compared with a currently used *** implications–The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives.A complex example of Welded Beam has been shown at the end of the *** implications–The algorithm would be useful for many design problems and social/industrial problems with conflicting ***/value–This paper presents two novel hybrid algorithms involving SPEA2 based on:local search;and Utopia point directed search *** concept has not been investigated before.

关键词： Evolutionary algorithms Boundary points multi-objective optimization problems Strength Pareto Evolutionary Algorithm 2(SPEA2)

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Mathematical study of trade-off relations in logistics systems

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2009年第1期232卷 122-126页

作者： Nanazawa, Youhei Suito, Hiroshi Kawarada, Hideo Okayama Univ Dept Environm & Math Sci Grad Sch Environm Sci Okayama 7008530 Japan Ryutsu Keizai Univ Dept Logist & Distribut Ryuugasaki Ibaraki 3018555 Japan

This paper presents a mathematical model of trade-off relations arising in third party logistics using Pareto optimal solutions for multi-objective optimization problems. The model defines an optimal set of distribution costs and service levels constituting a trade-off relation. An analogy to the concept of the indifference curve in the field of economics is discussed. Numerical experiments for a simplified problem are performed, demonstrating an increasing process of the utility of logistics. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Trade-off Logistics multi-objective optimization problems Pareto optimal solutions Indifference curve Non-dominated sorting Sharing function

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multi-objective Stochastic Paint Optimizer (MOSPO)

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NEURAL COMPUTING & APPLICATIONS 2022年第20期34卷 18035-18058页

作者： Khodadadi, Nima Abualigah, Laith Mirjalili, Seyedali Florida Int Univ Dept Civil & Environm Engn Miami FL 33199 USA Amman Arab Univ Fac Comp Sci & Informat Amman Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia King Abdulaziz Univ Jeddah Saudi Arabia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

The single-objective version of stochastic paint optimizer (SPO) is appropriately changed to solve multi-objective optimization problems described as MOSPO. Color theory, the color wheel, and color combination methods are the main concepts of SPO. The SPO will be able to do excellent exploration and exploitation thanks to four simple color combination rules that do not have any internal parameters. Principles like using of fixed-sized external archive make the recommended technique various from the initial single-objective SPO. In addition, to perform multi-objective optimization, the leader selection feature has been added to SPO. The efficiency of recommended multi-objective stochastic paint optimizer (MOSPO) is tested on ten mathematical (CEC-09) and eight multi-objective engineering design problems concerning remarkable precision and uniformity compared to multi-objective particle swarm optimization (MOPSO), multi-objective slap swarm algorithm (MSSA), and multi-objective ant lion optimizer. According to the results of different performance metrics, such as generational distance (GD), inverted generational distance (IGD), maximum spread, and spacing, the proposed algorithm can provide quality Pareto fronts with very competitive results with high convergence.

关键词： Stochastic paint optimizer multi-objective stochastic paint optimizer multi-objective optimization problems multi-objective engineering problems optimization Algorithm Benchmark Evolutionary computation

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multi-objective variation differential evolutionary algorithm based on fuzzy adaptive sorting

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ENERGY REPORTS 2022年 8卷 1020-1028页

作者： Mi, Xifeng Jiaozuo Normal Coll Jiaozuo 454000 Henan Peoples R China

In order to improve the convergence and diversity of multi-objective differential evolutionary algorithm in solving problems, a fuzzy adaptive sorting variation multi-objective differential evolution algorithm is proposed. First of all, using an adaptive fuzzy system by adjusting the parameters of the sorting variation, the balance of local search ability and global exploring ability of the algorithm, at the same time of accelerate the algorithm convergence speed, reduce the possibility of a fall in local optimum;Secondly, using the homogeneous population initialization method, based on the distribution of the algorithm was beginning to get a uniform initial population, improving the stability and diversity;Finally, add a temporary population to store is discarded by individuals, the optimized choice finally, for each generation to improve the population diversity in the process of evolution. Matlab was used to conduct simulation experiments and compared the proposed algorithm with four other multi-target evolutionary algorithms. The experimental results show that the proposed algorithm is superior to several other contrasting algorithms in convergence and diversity, and can effectively approach the frontier of real Pareto. At the same time, the experiment also verifies the validity of fuzzy adaptive sort variation strategy in the proposed algorithm. (c) 2022 Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： multi-objective optimization problems Differential evolution Sort variation Fuzzy systems Adaptive strategies

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A NOVEL multi-objective ESTIMATION OF DISTRIBUTION ALGORITHM BASED ON SENSITIVITY OF objective FUNCTION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2543-2566页

作者： Seo, Hyunshik Lee, Chaewoo Ajou Univ Dept Elect & Comp Engn San 5 Suwon 443749 South Korea

Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently proposed estimation of distribution algorithms (EDAs) build a probability distribution model based on the probabilistic information about decision variables of solutions, and then produce new solutions from the model. In the algorithms, the modeling technique enables the initial large search space to be reduced to small promising solution space during the search. However, the existing EDAs might be inefficient at generating the promising solutions since they depend on the information extracted from the decision variables of current solutions expected to approach the optimal solutions. For effective modeling of the promising solutions, we firstly introduce new information about the relationship between decision variables and objective functions;this information is called sensitivity of objective function. Secondly, we propose a multi-objective estimation of distribution algorithm based on the sensitivity of objective function (MOEDA-S). In the MOEDA-S, the sensitivity-based distribution modeling adapts to the current search strategy such that the convergence-focused search at the beginning part of the search is changed to a diversity-focused search at the latter part of the search. MOEDA-S is compared with two other leading multi-objective evolutionary algorithms on a set of test instances. The simulation results show that MOEDA-S outperforms the two compared algorithms in terms of both convergence and diversity performances of the solutions.

关键词： multi-objective optimization problems multi-objective evolutionary algorithms Estimation of distribution algorithms Variables sensitivity-based modeling

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Genetic Algorithm and Its Applications to Mechanical Engineering: A Review

Genetic Algorithm and Its Applications to Mechanical Enginee...

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4th International Conference on Materials Processing and Characterzation (ICMPC)

作者： Bhoskar, Trupti Kulkarni, Omkar K. Kulkarni, Ninad K. Patekar, Sujata L. Kakandikar, G. M. Nandedkar, V. M. Dnyanganga Coll Engn & Res Mech Engn Pune 41 Maharashtra India Savitribai Phule Pune Univ Dnyanganga Coll Engn & Res Mech Engn Pune 41 Maharashtra India Shri Guru Gobind Singhji Inst Engn & Technol Prod Engn Nanded Maharashtra India

Genetic Algorithm is optimization method based on the mechanics of natural genetics and natural selection. Genetic Algorithm mimics the principle of natural genetics and natural selection to constitute search and optimization procedures. GA is used for scheduling to find the near to optimum solution in short time. In a genetic algorithm representation is done with variable length of sub-chromosome. GA is developed to generate the optimal order scheduling solution. GA is used as tool in different processes to optimize the process parameters. This paper reviews the genetic algorithms that are designed for solving multiple problems in applications of material science and manufacturing in field of mechanical engineering. Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Genetic Algorithm Scheduling multi-objective optimization problems

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Thermal optimization Formulation Strategies for multi-Constraints Electronic Devices Placement on PCB

Thermal Optimization Formulation Strategies for Multi-Constr...

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IEEE Region 10 Conference 2009

作者： Ismail, Fatimah Sham Yusof, Rubiyah Univ Teknol Malaysia Fac Elect Engn Skudai Johor Malaysia Univ Teknol Malaysia Ctr Artificial Intelligent & Robot Kuala Lumpur Malaysia

ISBN: (纸本)9781424445462

This paper presents an algorithm for thermal optimization formulation strategies for multi-heat generation of integrated circuit (IC) on printed circuit board (PCB). Weighted-sum approach for multi-objective genetic algorithm (WMOGA) with formulated initial placement and multi-constraints parameters (FIPMCP) are presented. FIPMCP is used for the components selection and components to PCB placement mapping procedures for random initial population. The objectives are to optimize thermal distribution f(T) of electronic components on PCB and the PCB area f(A) needed simultaneously. For multi-objective optimization process, non-dominated optimal solutions and the best fitness WMOGA over iterations are plotted for both cases in order to obtain the best PCB optimal design solution. The results show that the best solution of f(T),f(A) and F(T,A) are minimized by 1.80%, 8.54% and 4.97% respectively.

关键词： electronic devices multi-objective optimization problems genetic algorithm

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