After the seminal books by Martello and Toth (1990) and Kellerer, Pferschy, and Pisinger
(2004), knapsack problems became a classical and rich research area in combinatorial …

This paper presents a state-of-the-art review on multi-objective metaheuristics for multi-
objective discrete optimization problems (MODOPs). The relevant literature source and their …

Image segmentation is among the most important techniques in image processing, and
many methods have been developed to perform this task. This paper presents a new multi …

This work investigates different Bayesian network structure learning techniques by
thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of …

The optimal selection of sensor structures improves the knowledge of the current plant state,
which is a central issue for the decision-making process. Instrumentation design is a …

Hybrid algorithms combine the best features of individual metaheuristics. They have proven
to find high-quality solutions for multi-objective optimization problems. Architec-tures provide …

The vehicle-environment cooperative (VEC) control has shown a great potential to improve
vehicle performance. Consequently, it is desirable to further investigate the incorporation of …

Knapsack problems are a highly active research area in combinatorial optimization.
Although, the knapsack problem has been known since over a century, see Mathews [368] …

This paper proposes a decomposition-based path-relinking method for multi-and many-
objective combinatorial optimization problems. The proposed approach, referred to as …

Abstract The Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm
(HMOBEDA) has shown to be very competitive for Many Objective Optimization Problems …