A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed, Gehad Ismail; Hassanien, Aboul Ella

doi:10.1007/s40747-018-0066-z

Cited by 72 publications

(27 citation statements)

References 42 publications

(42 reference statements)

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“…Thus, classical parametric techniques only compare the overall performance of algorithms. By contrast, nonparametric statistical tests can prove that the results are statistically significant and can perform valid comparisons without the assumption of the aforementioned properties . Accordingly, improved justifications on the correctness of the comparison are provided.…”

Section: Computational Resultsmentioning

confidence: 99%

“…In this section, 23 test problems are selected to evaluate the performance of the proposed CMVO algorithm. 9 A comprehensive experimental study is conducted on a large set of benchmark functions, which are illustrated in Table 2, including high-dimensional unimodal, high-dimensional multimodal, and composite multimodal problems. 21 The unimodal benchmark functions (F1-F7) only have a single global optimum and are easy to solve compared with the next categories.…”

Section: Results Of Cmvo Algorithmmentioning

confidence: 99%

“…The most popular techniques have elicited considerable attention in the search for a solution to accommodate concerns regarding optimization. The most popular individual‐based algorithms are hill climbing and simulated annealing (SA), whereas the most popular population‐based algorithms are particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) . Population‐based metaheuristics have some benefits over individual‐based algorithms because they have a high capability to explore and can reduce the probability to stick in local optima and cover all promising search space for information exchange between populations …”

Section: Introductionmentioning

confidence: 99%

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A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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The development of useful algorithms for solving global optimization problems has recently drawing the research community's attention. A number of optimization algorithms have been suggested, which mimic a particular biological process or imitate natural evolution. In this work, a novel population‐based optimization technique is proposed, the so‐called competitive multiverse optimizer (CMVO) for solving global optimization problems. This novel method is fundamentally inspired by the multiverse optimizer algorithm (MVO) but with a different framework. Our basic idea is to inject a pairwise competition mechanism between universes and adopts a novel update strategy that make universes learn from the winner. In CMVO, the mechanism of updating universes is not confined to the optimal universe but rather uses a bicompetitive scheme at each generation in which the universe that loses the competition learns from the winner, unlike in MVO in which all universes learn from the optimal universe. The main idea of this work is to raise the exploration rate for the search space by performing pairwise competition and improve the exploitation ability through learning from the winner. This work also presents a multi‐objective version of CMVO called MOCMVO with a simple structure to show the rapid convergence toward the Pareto front. The performance of our proposed method in single‐objective optimization is demonstrated on a large number of mathematical problems of standard benchmark. The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state‐of‐the‐art metaheuristic algorithms. In multiobjective optimization, the proposed MOCMVO is evaluated and compared to other multiobjective algorithms using 10 multiobjective benchmarks involving five unconstrained, three constrained, and two engineering design problems. The experimental results of the proposed method in multiobjective optimization demonstrate the competitiveness of the proposed method in term of quantitative and qualitative measures.

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Section: Computational Resultsmentioning

confidence: 99%

Section: Results Of Cmvo Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Benmessahel

Xie

Chellal

2020

Engineering Reports

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“…In the study of Jangir (2016) [13], used MFO in the solution of the emission constrained economic dispatch (ECED) problem, which is called as an allowable deviation in fuel cost and feasible tolerance in fuel cost. In the study in which MFO and Simulated Angeling were used together, Sayed (2018) [14] tried to solve the benchmark functions and the known engineering problems. It is possible to say that the PSO algorithm is generally used in the publications related to the training of ANFIS.…”

Section: Related Workmentioning

confidence: 99%

Training Anfis System with Moth-Flame Optimization Algorithm

Canayaz

2019

ijisae

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Adaptive Neuro Fuzzy Inference System (ANFIS) is an adaptive network that can use the computation and learning abilities of artificial neural network together with the inference feature of fuzzy logic. The ANFIS system, which is used in the solution of many problems such as classification and estimation of deep learning applications, meets the needs in many different areas such as modeling, control, and parameter estimation. In recent years, heuristic methods have been used for the training of this network, which requires initial and result parameters by its structure. Moth-Flame Optimization Algorithm (MFO) is one of the current heuristic methods modeled by the influence of the spiral movement of the moths towards the light source. In this study, the MFO algorithm was used for the first time for the optimization of initial and result parameters in the ANFIS system. In the determination of parameters, nonlinear system identification, time series estimation, classification problems were tried to be solved. When the results obtained for the ANFIS trained with the known heuristic methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Whale Optimization Algorithm (WOA) and the results of ANFIS trained by the MFO were examined, it was observed that the MFO had lower error values.

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“…Mirjalili et al [30] developed multi-verse algorithm based on the notion of cosmology. Sayed et al [31] introduced hybrid SA-MFO algorithm solving the engineering design problems. The genetic algorithm, differential evolution and bio-geographybased optimization are some of the popular examples of evolutionary concept [32].…”

Section: Introductionmentioning

confidence: 99%

A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

Tripathi

Mittal

Saxena

et al. 2020

Complex Intell. Syst.

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In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.

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A hybrid SA-MFO algorithm for function optimization and engineering design problems

Cited by 72 publications

References 42 publications

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Training Anfis System with Moth-Flame Optimization Algorithm

A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

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