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A comprehensive review on water cycle algorithm and its applications

NEURAL COMPUTING & APPLICATIONS 2020年第23期32卷 17433-17488页

作者： Nasir, Mohammad Sadollah, Ali Choi, Young Hwan Kim, Joong Hoon Islamic Azad Univ Dezful Branch Mat & Energy Res Ctr Dezful Iran Univ Sci & Culture Dept Mech Engn Tehran Iran Korea Univ Res Inst Mega Construct Seoul South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul South Korea

In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms.

关键词： Water cycle algorithm Metaheuristics Constrained optimization multi-objective optimization problems

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Development of a multi-objective artificial tree (MOAT) algorithm and its application in acoustic metamaterials

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MEMETIC COMPUTING 2020年第2期12卷 165-184页

作者： Li, Qiqi He, Zhichen Li, Eric Chen, Tao Wang, Qiuyu Cheng, Aiguo Changsha Univ Sci & Technol Coll Automot & Mech Engn Changsha 410114 Hunan Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Teesside Univ Sch Sci Engn & Design Middlesbrough Cleveland England Tangsteel Co Tech Ctr Tangshan Hebei Peoples R China

Although there are many algorithms that can solve the multi-objective optimization problems (MOPs) efficiently, each algorithm has its own disadvantages. The emergence of new algorithms is beneficial to make up the deficiencies of existing algorithms. Inspired by the organic matter transport process and the branch update theory of the banyan, this work proposed a new bio-inspired algorithm, named the multi-objective artificial tree (MOAT) algorithm to solve the MOPs. In MOAT, an improved crossover operator and an improved self-evolution operator are introduced to update solutions, a adaptive grid method is applied to manage the non-dominated solutions, and the strategy of variable number of branches in population is adopted to enhance the accuracy of this algorithm. Many typical test functions and seven well-known multi-objective algorithms, including MOEAD, NSGAII, MOPSO, GDE3, epsilon MOEA, IBEA and MPSO/D, are applied to study the accuracy and efficiency of MOAT. Experimental tests show that the results of MOAT are better than those of the seven algorithms, and the performance of MOAT is demonstrated. In addition, this new algorithm is also applied to solve the MOPs of two-dimensional acoustic metamaterials (AMs). The key parameters of AMs are optimized by MOAT to mitigate impact load and reduce structural mass, and the performance of these AMs is significantly improved.

关键词： multi-objective optimization problems Bio-inspired algorithm multi-objective artificial tree algorithm Acoustic metamaterials

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Intelligent workflow scheduling for Big Data applications in IoT cloud computing environments

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2021年第4期24卷 2957-2976页

作者： Abualigah, Laith Diabat, Ali Abd Elaziz, Mohamed Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia USM Sch Comp Sci Gelugor 11800 Penang Malaysia New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Dept Civil & Urban Engn Tandon Sch Engn Brooklyn NY 11201 USA Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt

Effective task scheduling is recognized as one of the main critical challenges in cloud computing;it is an essential step for effectively exploiting cloud computing resources, as several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and maximizing resource utilization. Task scheduling is an NP-hard problem, and consequently, finding the best solution may be difficult, particularly for Big Data applications. This paper presents an intelligent Big Data task scheduling approach for IoT cloud computing applications using a hybrid Dragonfly Algorithm. The Dragonfly algorithm is a newly introduced optimization algorithm for solving optimization problems which mimics the swarming behaviors of dragonflies. Our algorithm, MHDA, aims to decrease the makespan and increase resource utilization, and is thus a multi-objective approach. beta-hill climbing is utilized as a local exploratory search to enhance the Dragonfly Algorithm's exploitation ability and avoid being trapped in local optima. Two experimental studies were conducted on synthetic and real trace datasets using the CloudSim toolkit to compare MHDA to other well-known algorithms for solving task scheduling problems. The analysis, which included the use of a t-test, revealed that MHDA outperformed other well-known algorithms: MHDA converged faster than other methods, making it useful for Big Data task scheduling applications, and it achieved 17.12% improvement in the results.

关键词： Big Data task scheduling multi-objective optimization problems IoT cloud computing Dragonfly Algorithm

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Radar placement optimization based on adaptive multi-objective meta-heuristics

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Tema, Emrah Y. Sahmoud, Shaaban Kiraz, Berna Fatih Sultan Mehmet Vakif Univ Engn Fac Comp Engn Dept Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Data Sci Res & Applicat Ctr VEBIM Istanbul Turkiye

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%.

关键词： Airspace surveillance multi-objective optimization problems multi-objective meta-heuristics Radar coverage optimization Radar placement problem

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optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

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ENGINEERING optimization 2018年第4期50卷 716-731页

作者： Guo, Zhan Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai Peoples R China

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

关键词： p-Xylene oxidation process multi-objective optimization problems differential evolution algorithm population-based incremental learning algorithm self-adaptive

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multi-objective differential evolution based on normalization and improved mutation strategy

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NATURAL COMPUTING 2017年第4期16卷 661-675页

作者： Awad, Noor H. Ali, Mostafa Z. Duwairi, Rehab M. Nanyang Technol Univ Sch Elect & Elect Engn Elect & Elect Engn Singapore 639798 Singapore Jordan Univ Sci & Technol Sch Comp Informat Syst Comp Informat Syst Dept Irbid 22110 Jordan

Developing efficient algorithms for solving multi-objective optimization problems is a challenging and essential task in many applications. This task involves two or more conflicting objectives that need to be simultaneously optimized. Many real-world problems fall into this category. We introduce an improved version of multi-objective differential evolution (DE) algorithm, namely MOnDE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy "DE/rand-to-nbest" uses the best normalized individual in terms of all the objectives to guide the search towards the true pareto optimal solutions. As a result, the probability of producing superior solutions is increased and a faster convergence is achieved. Summation of normalized objective values method is used instead of non-domination sorting to overcome the high computational complexity and overhead problems of sorting non-dominated solutions. The performance of our approach is tested on a set of benchmark problems that consist of two to five objectives. Different combinations of multi-objective evolutionary programming and multi-objective differential evolution algorithms have been used for comparisons. The results affirm the efficiency and robustness of the proposed approach among other well-known algorithms from the literature.

关键词： multi-objective optimization problems Differential evolution Summation of normalized objective values method and multi-objective evolutionary programming

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MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

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JOURNAL OF GLOBAL optimization 2016年第4期66卷 729-768页

作者： Marti, Luis Garcia, Jesus Berlanga, Antonio Molina, Jose M. Univ Fed Flumense Inst Comp Ave Gal Milton Tavares de Souza BR-24210346 Rio De Janeiro Brazil Univ Carlos III Madrid Grp Appl Artificial Intelligence Dept Informat Ave Univ Carlos 322 Madrid 28270 Spain

The extension of estimation of distribution algorithms (EDAs) to the multi-objective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding "standard" multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the model-building issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements.

关键词： multi-objective optimization problems Estimation of distribution algorithms Model-building problem Neural networks Growing neural gas

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multi-objective-based differential evolution for balancing production cost, diversity and aggregated performance attributes in product family design

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2024年第1期36卷 175-223页

作者： Ali, Ismail M. Turan, Hasan H. Chakrabortty, Ripon K. Elsawah, Sondoss Univ New South Wales Capabil Syst Ctr Sch Engn & IT Northcott Dr Desk 1 Room 242 Campbell Canberra ACT 2612 Australia

In product family design (PFD), deciding on a platform design strategy can be viewed as a multidisciplinary optimization problem that involves several factors, such as design variables, manufacturing costs, customizability, supplier reliability, and customer satisfaction. In this study, a multi-objective based differential evolution (MO-based DE) algorithm has been proposed for tackling the module-based PFD problem. The MO-based DE aims to find the best balance between many objectives, such as total production cost, diversity index, and a combination of other objectives (performance attributes). These objectives include commonality, modularity, and suppliers' reliability and all are aggregated to provide a goodness score. To effectively improve the DE's efficiency while solving such a complex optimization problem, the proposed DE integrates new elements such as (i) a novel solution representation, (ii) an improved heuristic technique for platform development, (iii) a weighted aggregation to combine different objectives, and (iv) a proposed platform-based crossover. To validate its performance, the proposed MO-based DE has been compared with (1) the standard DE to assess the effect of the incorporated new elements on DE's performance, and (2) well-known fast non-dominant sorting genetic algorithms NSGA-II and (3) NSGA-III for solving a real case study of a family of kettles. The experimental results confirmed the efficacy of the proposed MO-based DE as follows: in terms of average cost value, MO-based DE outperformed standard DE and NSGA-II by 26.40% and 11.69%, respectively. While in terms of goodness score, it achieved 20.69% and 8.05% better scores compared to standard DE and NSGA-II, respectively. Moreover, the proposed MO-based DE attained a very competitive performance against NSGA-III as it reached a better average cost and goodness score of 1.74% and 0.82%, respectively.

关键词： Product family design Differential evolution multi-objective optimization problems Supplier' reliability Production cost

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A decomposition-based ant colony optimization algorithm for the multi-objective community detection

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第1期11卷 173-188页

作者： Ji, Ping Zhang, Shanxin Zhou, ZhiPing Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Jiangnan Univ Minist Educ Engn Res Ctr Internet Things Technol Applicat Wuxi 214122 Peoples R China

Community structure is an important feature of complex network and detecting community can help us understand the function of networks very well. Community detection can be considered as a multi-objective optimization problem and the heuristic operators have shown promising results in dealing with this problem. In this paper, a multi-objective community detection algorithm named MOCD-ACO is proposed by combining the heuristic operator of ant colony optimization (ACO) and the multi-objective evolutionary algorithmbased on decomposition (MOEA/D). MOCD-ACO can simultaneously decompose two objective functions, i.e., Negative Ratio Association and Ratio Cut, into a number of single-objective optimization problems. Each ant is responsible for searching for a solution to a sub-problem. All ants are divided into some groups, each group sharing a pheromone matrix. The ants use pseudo-random probability selection models to construct solutions. An ant updates its current solution if it has found a better one in terms of its own objective. To make the algorithm not easy to fall into the local optimal solution, the weighted simulated annealing local search operator is integrated into the framework to expand the search range. In the experiments, synthetic network datasets and real network datasets are used to evaluate the performance of MOCD-ACO. Compared with five state-of-the-art methods, our algorithm proves to be effective in terms of normalized mutual information and modularity.

关键词： Ant colony optimization Decompose multi-objective optimization problems Community detection Complex network

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MOSMA: multi-objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting

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IEEE ACCESS 2021年 9卷 3229-3248页

作者： Premkumar, Manoharan Jangir, Pradeep Sowmya, Ravichandran Alhelou, Hassan Haes Heidari, Ali Asghar Chen, Huiling GMR Inst Technol Dept Elect & Elect Engn Rajam 532127 India Rajasthan Rajya Vidyut Prasaran Nigam Ltd Sikar 332025 India Natl Inst Technol Tiruchirappalli Dept Elect & Elect Engn Tiruchirappalli 620015 India Tishreen Univ Fac Mech & Elect Engn Dept Elect Power Engn Latakia 2230 Syria Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran *** Iran Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

This paper proposes a multi-objective Slime Mould Algorithm (MOSMA), a multi-objective variant of the recently-developed Slime Mould Algorithm (SMA) for handling the multi-objective optimization problems in industries. Recently, for handling optimization problems, several meta-heuristic and evolutionary optimization techniques have been suggested for the optimization community. These methods tend to suffer from low-quality solutions when evaluating multi-objective optimization (MOO) problems than addressing the objective functions of identifying Pareto optimal solutions' accurate estimation and increasing the distribution throughout all objectives. The SMA method follows the logic gained from the oscillation behaviors of slime mould in the laboratory experiments. The SMA algorithm shows a powerful performance compared to other well-established methods, and it is designed by incorporating the optimal food path using the positive-negative feedback system. The proposed MOSMA algorithm employs the same underlying SMA mechanisms for convergence combined with an elitist non-dominated sorting approach to estimate Pareto optimal solutions. As a posteriori method, the multi-objective formulation is maintained in the MOSMA, and a crowding distance operator is utilized to ensure increasing the coverage of optimal solutions across all objectives. To verify and validate the performance of MOSMA, 41 different case studies, including unconstrained, constrained, and real-world engineering design problems are considered. The performance of the MOSMA is compared with multiobjective Symbiotic-Organism Search (MOSOS), multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and multiobjective Water-Cycle Algorithm (MOWCA) in terms of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), Spacing, and Run-time. The simulation results demonstrated the superiority of the proposed algorithm in realizing h

关键词： Constrained multi-objective optimization problems multi-objective slime mould algorithm (MOSMA) real-world problems slime mould algorithm (SMA) unconstrained

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