A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME

Santiago, Alejandro; Dorronsoro, Bernabè; Nebro, Antonio J.; Durillo, Juan J.; Castillo, Oscar; Fraire, Héctor

doi:10.1016/j.ins.2018.09.005

Cited by 77 publications

(28 citation statements)

References 22 publications

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“…In addition to choosing the best mutation mechanism, using an appropriate adaptive selection of DE operators method is also important as it can enhance the performance of DE for a constrained optimization problem [47]. Without such a scheme, a DE algorithm may be trapped in a local optimum which could also lead to premature convergence when solving hard constraints.…”

Section: ) Multi-operator De Variantsmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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Section: ) Multi-operator De Variantsmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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“…An optimization problem can have one or more objective functions. The framework of a multi-objective function can be defined based on the following equation [35]:…”

Section: Multi-objective Algorithmsmentioning

confidence: 99%

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

et al. 2019

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The estimation of solar radiation for planning current and future periods in different fields, such as renewable energy generation, is very important for decision makers. The current study presents a hybrid model structure based on a multi-objective shark algorithm and fuzzy method for forecasting and generating a zone map for solar radiation as an alternative solution for future renewable energy production. The multi-objective shark algorithm attempts to select the best input combination for solar radiation (SR) estimation and the optimal value of the adaptive neuro-fuzzy inference system (ANFIS) parameter, and the power parameter of the inverse distance weight (IDW) is computed. Three provinces in Iran with different climates and air quality index conditions have been considered as case studies for this research. In addition, comparative analysis has been carried out with other models, including multi-objective genetic algorithm-ANFIS and multi-objective particle swarm optimization-ANFIS. The Taguchi model is used to obtain the best value of random parameters for multi-objective algorithms. The comparison of the results shows that the relative deviation index (RDI) of the distributed solutions in the Pareto front based multi-objective shark algorithm has the lowest value in the spread index, spacing metric index, favorable distribution, and good diversity. The generated Pareto solutions based on the multi-objective shark algorithm are compared to those based on the genetic algorithm and particle swarm algorithm and found to be the optimal and near ideal solutions. In addition, the determination of the best solution based on a multi-criteria decision model enables the best input to the model to be selected based on different effective parameters. Three different performance indices have been used in this study, including the root mean square error, Nash–Sutcliffe efficiency, and mean absolute error. The generated zone map based on the multi-objective shark algorithm-ANFIS highly matches with the observed data in all zones in all case studies. Additionally, the analysis shows that the air quality index (AQI) should be considered as effective input for SR estimation. Finally, the measurement and analysis of the uncertainty based on the multi-objective shark algorithm-ANFIS were carried out. As a result, the proposed new hybrid model is highly suitable for the generation of accurate zone mapping for different renewable energy generation fields. In addition, the proposed hybrid model showed outstanding performance for the development of a forecasting model for the solar radiation value, which is essential for the decision-makers to draw a future plan for generating renewable energy based solar radiation.

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“…Adaptive methods learn from previous selection stages and use any obtained information to update their selection criterion. Several adaptive methods have been previously presented, such as Random Descent, Permutation Descent, Choice Function (Cowling, Kendall & Soubeiga, 2000), Tabu Search (Dowsland, Soubeiga & Burke, 2007) or Roulette Wheel with updated probabilities (Baykasoğlu & Ozsoydan, 2017;Santiago et al, 2019). Meanwhile, non-adaptive methods only consider currently existing data to perform its selection, such as Simple Random or Greedy (Cowling, Kendall & Soubeiga, 2000).…”

Section: Hyper-heuristicsmentioning

confidence: 99%

“…A Fast Multi-objective Hyper-heuristic Genetic Algorithm (MHypGA), proposed in Kumari, Srinivas, and Gupta (2013), uses reinforced learning with adaptive weights to select from a set of low-level heuristics based on different combinations of selection, crossover and mutation operators. A hyper-heuristic that uses a fuzzy logic engine named Fuzzy Adaptive Multi-objective Evolutionary algorithm (FAME) (Santiago et al, 2019) has been recently proposed. This methodology manages several reproduction operators as low-level heuristics.…”

Section: Hyper-heuristicsmentioning

confidence: 99%

“…The application of hyper-heuristics to solve single and multiple-objective optimization problems have been proposed before with satisfying results (Burke, Silva & Soubeiga, 2005;García, 2010;Maashi, Özcan & Kendall, 2014;Maashi, Kendall & Özcan, 2015;Zamli, Din, Kendall & Ahmed, 2017;Santiago et al, 2019). Hyper-heuristics have also been applied to tackle dynamic single-objective problems (Baykasoğlu & Ozsoydan, 2017;Topcuoglu, Ucar & Altin, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Application of Population Evolvability in a Hyper-Heuristic for Dynamic Multi-Objective Optimization

Macias-Escobar

Cruz-Reyes

Dorronsoro

et al. 2019

Technological and Economic Development of Economy

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It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems.

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A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME

Cited by 77 publications

References 22 publications

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy

Application of Population Evolvability in a Hyper-Heuristic for Dynamic Multi-Objective Optimization

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