Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-bou...

The utilization of a specific priority rule in scheduling operations flexible job shop systems strongly influences production goals. In context control real practice, performance indicators are evaluated always en bloc. This paper addresses the multi-criteria evaluating five selected conflicting objectives via scalar simulation-based optimization related to applied rule. It is connected discret...

Overparameterization and overfitting are common concerns when designing training deep neural networks, that often counteracted by pruning regularization strategies. However, these strategies remain secondary to most learning approaches suffer from time computational intensive procedures. We suggest a multiobjective perspective on the of networks treating its prediction accuracy network complexi...

A general duality framework in convex multiobjective optimization is established using the scalarization with K-strongly increasing functions and the conjugate duality for composed convex cone-constrained optimization problems. Other scalarizations used in the literature arise as particular cases and the general duality is specialized for some of them, namely linear scalarization, maximum(-line...

As large amounts of data are circulated both from users to a cloud server and between users, privately aggregating the shared is critical. This article considers problem private weighted sum aggregation with secret weights, where an aggregator wants compute local some agents. Based on privacy requirements posed weights, there different secure multiparty computation schemes exploiting kno...

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is challenging real-world problem. This recently introduced problem variant considers exceptional parts, intercellular moves, transportation times, sequence-dependent family setup and recirculation requiring minimization of makespan total tardiness, simultaneously. A previous study shows that the exact solv...

A parametric algorithm for identifying the Pareto set of a biobjective integer program is proposed. The algorithm is based on the weighted Chebyshev (Tchebycheff) scalarization, and its running time is asymptotically optimal. A number of extensions are described, including: a technique for handling weakly dominated outcomes, a Pareto set approximation scheme, and an interactive version that pro...

A procedure to approximate the nondominated set for general (continuous) bi-criteria programs is proposed. The piecewise approximation is composed of quadratic curves, each of which is developed locally in a neighborhood of a nondominated point of interest and based on primal-dual relationships associated with the weighted-Tchebycheff scalarization of the original problem. The approximating qua...