In this paper, we propose a hybrid fine-tuned multiobjective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a...

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In this paper, we have proposed an improved genetic algorithm (GA) to solve constrained knapsack problem in fuzzy environment. Some of the objects among all the objects are associated with a discount. If at least a predetermined quantity of the object(s) (those are associated with a discount) is selected, then an amount (in $) is considered as discount. The aim of the model is to maximize the t...

A knapsack problem is to select a set of items that maximizes the total profit selected while keeping weight no less than capacity knapsack. As generalized form with multiple knapsacks, multi-knapsack (MKP) disjointed for each To solve MKP, we propose deep reinforcement learning (DRL) based approach, which takes as input available capacities profits and weights items, normalized unselected dete...

We proposed the theory of multi-fuzzy sets [Sabu and Ramakrishnan (2010a), (2011a)] as an extension of theories of fuzzy sets, L-fuzzy sets [Goguen (1967)] and intuitionistic fuzzy sets [Atanassov (1986)]. Theory of multi-fuzzy sets deals with multi-level fuzziness and multi-dimensional fuzziness. Our previous papers discussed the basic notions of multi-fuzzy sets, multi-fuzzy topology, and mul...

abstract— in this paper, a new extended method of multi criteria decision making based on fuzzy-topsis theory is introduced. fuzzy mcdm algorithm for determining the best choice among all possible choices when the data are fuzzy is also presented. using a new index leads to procedure for choosing fuzzy ideal and negative ideal solutions directly from the fuzzy data observed alternatives.in this...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...