An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems

Yi, Jiao-Hong; Deb, Suash; Dong, Junyu; Alavi, Amir H.; Wang, Gai-Ge

doi:10.1016/j.future.2018.06.008

Cited by 171 publications

(52 citation statements)

References 64 publications

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“…The optimal distribution center points found by IGA algorithm for 6 and 10 distribution centers are (10,22,21,2,20,17) and (30,23,14,1,2,11,25,24,15,4). The optimal distribution center points found by CCS algorithm for 6 and 10 distribution centers are (23,22,21,16,15,20) and (6,10,23,14,22,25,7,16,15,20).…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Optimization problems have been one of the most important research topics in recent years. They exist in many domains, such as scheduling [1,2], image processing [3][4][5][6], feature selection [7][8][9] and detection [10], path planning [11,12], feature selection [13], cyber-physical social system [14,15], texture discrimination [16], saliency detection [17], classification [18,19], object extraction [20], shape design [21], big data and large-scale optimization [22,23], multi-objective optimization [24], knapsack problem [25][26][27], fault diagnosis [28][29][30], and test-sheet composition [31]. Metaheuristic algorithms [32], a theoretical tool, are based on nature-inspired ideas, which have been extensively used to solve highly non-linear complex multi-objective optimization problems [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

View full text Add to dashboard Cite

show abstract

“…In [36] BiPOP-CMA-ES is proposed which is a Multi-start CMA-ES with equal budgets for two interlaced restart strategies. In addition, many improved methods for optimization algorithms are introduced in [44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

Dehghani

Samet

2020

SN Appl. Sci.

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A novel optimization methodology, Momentum Search Algorithm (MSA) is presented based on Newton's laws: the law of conservation of momentum. It includes a set of masses in a closed system considering the conservation of momentum and kinetic energy of bodies. The possible solutions are presented by system bodies' positions in an n-dimensional space. The mass of bodies is proportional to their fitness function. Larger masses represent the better solutions. At each iteration, an external body collides separately with all solution bodies and moves them toward the optimum solution. The direction of the collision depends on the position of solution bodies and the position of the body with the best fitness function. As the better solutions have heavier bodies, the external body has less effect on their positions. On the other hand, the worse solutions are lighter and moved easily by the external body toward the better positions. The best position is achieved by allowing the external body to move the solution bodies toward better positions. The numerical results obtained from several standard benchmark test functions indicate the superiority of the proposed method over many other optimization techniques such as Genetic Algorithm,

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“…In general, the estimation parameters were decided by operation or experimental data. The inverse problem was usually implemented by artificial intelligence algorithms, which had the function of learning and reasoning, such as the simulated annealing algorithm, tabu search algorithm, fuzzy clustering algorithm, improved NSGA-III algorithm [1], krill herd algorithm [2][3][4], and so on. For example, Nino-Ruiz proposed two efficient and practical implementations of local search methods based on tabu search and simulated annealing to solve inverse problem [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

Zhong

2019

Information

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Establishing an accurate inverse model is a key problem in the design of adaptive inverse controllers. Most real objects have nonlinear characteristics, so mathematical expression of an inverse model cannot be obtained in most situation. A Takagi–Sugeno(T-S)fuzzy model can approximate real objects with high precision, and is often applied in the modeling of nonlinear systems. Since the consequent parameters of T-S fuzzy models are linear expressions, this paper firstly uses a fuzzy c-regression model (FCRM) clustering algorithm to establish inverse fuzzy model. As the least mean square (LMS) algorithm is only used to adjust consequent parameters of the T-S fuzzy model in the process of parameter adjustment, the premise parameters are fixed and unchanged in the process of adjustment. In this paper, the back propagation (BP) algorithm is applied to adjust the premise and consequent parameters of the T-S fuzzy model, simultaneously online. The simulation results show that the error between the system output controlled by proposed adaptive inverse controller and the desired output is smaller, also the system stability can be maintained when the system output has disturbances.

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An improved NSGA-III algorithm with adaptive mutation operator for Big Data optimization problems

Cited by 171 publications

References 64 publications

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

Adaptive Inverse Controller Design Based on the Fuzzy C-Regression Model (FCRM) and Back Propagation (BP) Algorithm

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