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A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for multi-objective Optimization

ALGORITHMS 2019年第12期12卷 261页

作者： AbdelAziz, Amr Mohamed Soliman, Taysir Hassan A. Ghany, Kareem Kamal A. Sewisy, Adel Abu El-Magd Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf 62111 Egypt Assiut Univ Fac Comp & Informat Assiut 71516 Egypt Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

multi-objective problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

关键词： multi-objective Optimization multi-objective problems Pareto Optimization Swarm Intelligence Tabu Search Whale Optimization Algorithm

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On the Real World Applications of Many-objective Evolutionary Algorithms 18

On the Real World Applications of Many-Objective Evolutionar...

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1rst International Conference on Data Science, E-Learning and Information Systems (DATA)

作者： Safi, Hayder H. Ucan, Osman N. Bayat, Oguz Altinbas Univ Dept Elect & Comp Engn Istanbul Turkey

ISBN: (纸本)9781450365369

Recently, many-objective evolutionary algorithms have been studied by many researchers. Most of research works were concentrated on developing new methods that can deal with the high number of objective functions of such complex problems. Proposing new scalable benchmarks and valid performance metrics are another two commonly discussed domains in many-objective optimization problems. Most of proposed algorithms have been used to solve different real-world problems from several domains and applications. There are many research papers were published to review and compare the performance of current many-objective evolutionary algorithms. On the other hand there is no work that presented to highlight and review the usage of such algorithms in real problems and applications. Therefore there is an increasing significance for analyzing and reviewing of the complex real world problems that solved using many-objective optimization evolutionary algorithm. In this paper, we review the recently research work that have been done in the domain of solving complex real-world problems using many objective evolutionary algorithms. In addition, the most important issues of metrics, benchmarks and algorithms will be discussed briefly.

关键词： Many-objective optimization evolutionary algorithms multi-objective problems real-world optimization problems

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Study The Effect of High Dimensional objective Functions on multi-objective Evolutionary Algorithms 18

Study The Effect of High Dimensional Objective Functions on ...

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4th International Conference on Engineering and MIS (ICEMIS)

作者： Safi, Hayder H. Ucan, Osman N. Bayat, Oguz Altinbas Univ Dept Elect & Comp Engn Istanbul Turkey

ISBN: (纸本)9781450363921

multi-objective Evolutionary Algorithms (MOEAs) have been widely studied by many researchers and they have been used to solve different real-world applications that have more than one objective function. However, most MOEAs work well only when the number of objective functions is small such as two or three. The performance of MOEAs starts degrading significantly when number of objective functions increases. Therefore, there is increasing importance for studying and analyzing the effect of increasing the number of objective functions on the performance of current multi objective evolutionary algorithms. In this paper, the performance of three state-of-the-art multi objective evolutionary algorithms is investigated when increasing the number of objective functions. The tested algorithms are analyzed using test DTLZ test suit. The results show that SMPSO and NSGA-II algorithms are the best two algorithms for high number of objective functions. In addition, the results show that the running time of SMPSO and GDE3 algorithms was effected and increased much when the number of objective functions is large.

关键词： NSGA-II Differential Evolution algorithm PSO multi-objective problems evolutionary algorithms

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Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times

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SCIENTIA IRANICA 2018年第4期25卷 2233-2253页

作者： Mousavi, S. M. Mahdavi, I Rezaeian, J. Zandieh, M. Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran Shahid Beheshti Univ GC Management & Accounting Fac Dept Ind Management Tehran Iran

The production scheduling problem in hybrid flow shops is a complex combinatorial optimization problem observed in many real-world applications. The standard hybrid flow shop problem often involves unrealistic assumptions. In order to address the realistic assumptions, four additional traits were added to the proposed problem. These include the re-entrant line, setup times, position-dependent learning effects, and consideration of maximum completion time together with total tardiness as an objective function. Since the proposed problem is NP-hard, a meta-heuristic algorithm is proposed as the solution procedure. The solution procedure is categorized as an a priori approach. To show the efficiency and effectiveness of the proposed algorithm, computational experiments were carried out on various test problems. Computational results show that the proposed algorithm can obtain an effective and appropriate solution quality for our investigated problem. (C) 2018 Sharif University of Technology. All rights reserved.

关键词： Re-entrant hybrid flow shop Setup times Learning effect multi-objective problems A priori approach

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Aesthetic considerations for the min-max K-Windy Rural Postman Problem

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NETWORKS 2017年第3期70卷 216-232页

作者： Corberan, Angel Golden, Bruce Lum, Oliver Plana, Isaac Sanchis, Jose M. Univ Valencia Dept Estadist & Invest Operat Valencia Spain Univ Maryland RH Smith Sch Business College Pk MD 20742 USA Univ Maryland Dept Appl Math & Sci Computat College Pk MD 20742 USA Univ Valencia Dept Matemat Econ & Empresa Valencia Spain Univ Politecn Valencia Dept Matemat Aplicada Valencia Spain

The aesthetic quality of routes is a feature of route planning that is of practical importance, but receives relatively little attention in the literature. Several practitioners have pointed out that the visual appeal of a proposed set of routes can have a strong influence on the willingness of a client to accept or reject a specific routing plan. While some work has analyzed algorithmic performance relative to traditional min-sum or min-max objective functions and aesthetic objective functions, we are not aware of any work that has considered a multi-objective approach. This work considers a multi-objective variant of the Min-Max K-Vehicles Windy Rural Postman Problem, discusses several formulations of the problem, and presents computational experiments with a heuristic algorithm. After exploring several formulations, we choose to study the problem with a bi-objective function that includes contributions from the route overlap index and average task distance aesthetic measures. The heuristic extends the cluster-first procedure presented in Lum et al. (Networks 69 (2017), 290-303) by incorporating the new objective function into the improvement phase and adding a perturbation routine. (c) 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(3), 216-232 2017

关键词： arc routing problems min-max objective aesthetic objective multi-objective problems

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Correlative Particle Swarm Optimization for multi-objective problems

Correlative Particle Swarm Optimization for Multi-objective ...

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2nd International Conference on Swarm Intelligence (ICSI)

作者： Shen, Yuanxia Wang, Guoyin Liu, Qun Chongqing Univ Posts & Telecommun Inst Comp Sci & Technol Chongqing 400065 Peoples R China

ISBN: (纸本)9783642215230

Particle swarm optimization (PSO) has been applied to multi-objective problems. However, PSO may easily get trapped in the local optima when solving complex problems. In order to improve convergence and diversity of solutions, a correlative particle swarm optimization (CPSO) with disturbance operation is proposed, named MO-CPSO, for dealing with multi-objective problems. MO-CPSO adopts the correlative processing strategy to maintain population diversity, and introduces a disturbance operation to the non-dominated particles for improving convergence accuracy of solutions. Experiments were conducted on multi-objective benchmark problems. The experimental results showed that MO-CPSO operates better in convergence metric and diversity metric than three other related works.

关键词： multi-objective problems Correlative particle swarm optimization Population diversity

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Numerical comparisons of migration models for multi-objective Biogeography-Based Optimization

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INFORMATION SCIENCES 2016年 328卷 302-320页

作者： Guo, Weian Wang, Lei Wu, Qidi Tongji Univ Sinogerman Coll Appl Sci Shanghai 200092 Peoples R China Tongji Univ Dept Elect & Informat Shanghai 201804 Peoples R China

In current years, Biogeography-Based Optimization (BBO), a novel Evolutionary Algorithm (EA), has drawn a lot of attention due to its dramatic performance. In our previous work, BBO's migration models for single-objective problem (SOP) have been investigated to reveal their effects to algorithm's performance. However to date, there is a few investigation about migration models for multi-objective problems (MOPs) which are common in practice though more difficult. To make BBO competitive in dealing with MOPs, migration models of BBO are explored and exploited in this paper. One contribution of our work is that we propose multi-objective BBO (MOBBO). By comparing MOBBO with other popular MOEAs, this algorithm is competent to handle MOPs. Besides, we present and compare six principal migration models. In comparisons, we find that Trapezoidal Migration Model performs well for MOPs, while its performance is inferior to other migration models for SOPs. Besides, Quadratic Migration Model's performance for MOPs is worse, while it has a good performance to solve SOPs. These demonstrate that the conclusion to evaluate migration models for SOPs does not hold for MOPs, so another contribution in this paper is that we reevaluate migration models for MOPs in an empirical way, which is helpful to design migration models for MOBBO. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Biogeography-Based Optimization Evolutionary Algorithm multi-objective problems Migration models

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Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier

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APPLIED MATHEMATICS AND COMPUTATION 2015年 258卷 436-453页

作者： Shams, Masumeh Rashedi, Esmat Hakimi, Ahmad Grad Univ Adv technol Dept Elect Engn Kerman Iran Shahid Bahonar Univ Dept Elect Engn Kerman Iran

Gravitational search algorithm (GSA) is a recent introduced algorithm which is inspired by law of gravity and mass interactions. In this paper, a novel version of GSA, named Clustered- GSA, is proposed to reduce complexity and computation of the standard GSA. This algorithm is originated from calculating central mass of a system in nature and improves the ability of GSA by reducing the number of objective function evaluations. Clustered-GSA is evaluated on two sets of standard benchmark functions and the results are compared with several heuristic algorithms and a deterministic optimization algorithm. Experimental results show that by using Clustered-GSA, better results are achieved with lower complexity. Moreover, the proposed algorithm is used to optimize the parameters of a Low Noise Amplifier (LNA) in order to achieve the required specifications. LNA is the first stage in a receiver after the antenna. The main performance characteristics of receivers are dictated by the LNA performance. It is necessary to study, design, and optimize all the elements included in the structure, simultaneously. The comparative results show the efficiency of the proposed algorithm. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Optimization Gravitational search algorithm Clustered-GSA multi-objective problems Low noise amplifier

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Parameter Tuning of MOEAs Using a Bilevel Optimization Approach 8

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8th International Conference on Evolutionary multi-Criterion Optimization (EMO)

作者： Andersson, Martin Bandaru, Sunith Ng, Amos Syberfeldt, Anna Univ Skovde Virtual Syst Res Ctr Skovde Sweden

ISBN: (纸本)9783319159348;9783319159331

The performance of an Evolutionary Algorithm (EA) can be greatly influenced by its parameters. The optimal parameter settings are also not necessarily the same across different problems. Finding the optimal set of parameters is therefore a difficult and often time-consuming task. This paper presents results of parameter tuning experiments on the NSGA-II and NSGA-III algorithms using the ZDT test problems. The aim is to gain new insights on the characteristics of the optimal parameter settings and to study if the parameters impose the same effect on both NSGA-II and NSGA-III. The experiments also aim at testing if the rule of thumb that the mutation probability should be set to one divided by the number of decision variables is a good heuristic on the ZDT problems. A comparison of the performance of NSGA-II and NSGA-III on the ZDT problems is also made.

关键词： Parameter tuning NSGA-II NSGA-III ZDT Bilevel optimization multi-objective problems

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Soft-Computing: An innovative technological solution for urban traffic-related problems in modern cities

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2014年第Nov.期89卷 236-244页

作者： Salcedo-Sanz, S. Cuadra, L. Alexandre-Cortizo, E. Jimenez-Fernandez, S. Portilla-Figueras, A. Univ Alcala de Henares Dept Signal Theory & Commun Madrid 28871 Spain

Urban traffic-related problems are a major point of concern in the majority of cities in the world. These problems arise in many different aspects, such as providing fast congestion-free routes in a city (improving the mobility of its inhabitants), or solving problems caused by the continuous presence of vehicles on the road (reducing thus the levels of noise and CO2 emission). Many of these problems can be mathematically expressed as optimization, classification or regression models, and involve, in most of the cases, huge search-spaces or hard constraints. In this paper we discuss one of these problems, the so called Reconfiguration One-Way Traffic Optimization Problem (ROWTOP). The problem consists in optimizing the directions of one-way streets in a city, in those cases in which this reconfiguration is needed because of the appearance of a major problem that involves prolonged street cuts. The problem is defined as a multi-objective optimization case and is solved by using a Soft-Computing (SC) approach based on an evolutionary algorithm (the NSGA-II technique). Its performance is discussed in a real problem in a Spanish city, achieving excellent results and showing the feasibility of these techniques as an innovative technology-based approaches, able to upgrade cities without incurring in exorbitant expenditures, helping politicians in making decisions and, ultimately, making cities more sustainable and better places to live. (C) 2013 Elsevier Inc. All rights reserved.

关键词： Soft-Computing Urban traffic Evolutionary computation multi-objective problems NSGA-II

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