Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private. With an increase in its adoption, a growing concern is related to economic and environmental cost (as also case other techniques). Unfortunately, little work been done optimize energy consumption emissions of carbon dio...

Some of industrial and real life problems are difficult to be solved by traditional methods, because they need exponential number calculations. As an example, we can mention decision-making problems. They defined as optimization Ant Colony Optimization (ACO) is between the best that solves combinatorial The method mimics behavior ants in nature, when look for a food. One algorithm parameters ca...

The 0-1 multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with several real-life applications, for example, in project selection. Genetic algorithms (GA) are effective heuristics for solving the 0-1 MKP. Multiple individual GAs with specific characteristics have been proposed in literature. However, so far, these approaches have only been partially compa...

Multiple Knapsack Problem (MKP) is a most popular multiple subset selection problem that belongs to the class of NP-Complete problems. The aim is to assign optimal subsets among all original items to some knapsacks, such that the overall profit of all selected items be maximised, while the total weight of all assigned items to any knapsack does not exceed the allowable capacity of it. Artificia...

The quadratic multiple knapsack problem (QMKP) consists in assigning a set of objects, which interact through paired profit values, exclusively to different capacity-constrained knapsacks with the aim of maximising total profit. Its many applications include the assignment of workmen to different tasks when their ability to cooperate may affect the results. Strategic oscillation (SO) is a searc...

The 0/1 Multiple Knapsack Problem is an important class of combinatorial optimization problems, and various heuristic and exact methods have been devised to solve it. Genetic Algorithm (GA) shows good performance on solving static optimization problems. However, sometimes lost of diversity makes GA fail adapt to dynamic environments where evaluation function and/or constraints or environmental ...

In this paper, we study the robustness over the cardinality variation for the knapsack problem. For the knapsack problem and a positive number α ≤ 1, we say that a feasible solution is α-robust if, for any positive integer k, it includes an α-approximation of the maximum k-knapsack solution, where a k-knapsack solution is a feasible solution that consists of at most k items. In this paper, we s...