In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost considered. H-CRANs serve groups of users through femto-cell points (FAPs) remote heads (RRHs) equipped with massive multiple input output (MIMO) connected to base-...

In practical applications of optimization it is common to have several conflicting objective functions to optimize. Frequently, these functions are subject to noise or can be of black-box type, preventing the use of derivative-based techniques. We propose a novel multiobjective derivative-free methodology, calling it direct multisearch (DMS), which does not aggregate any of the objective functi...

In this paper, a Goal Programming (GP) model is converted into a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. To solve the resulting MOO problem, a hybrid metaheuristic with two steps is proposed to find the Pareto set’s solutions. First, a Record-to-Record Travel with an adaptive memory is used to find first non-dominated Pareto frontier solutions preem...

Nowadays, sustainability is one of the key elements which should be considered in energy systems. Such systems are essential any manufacturing system to supply requirements those To optimize consumption system, various applications have been developed literature, with a number pros and cons. In addition, majority such applications, multi-objective optimization (MOO) plays an outstanding role. r...

In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutual dependencies such that disruption of importan...

The multi-objective optimization (MOO) of complex systems remains a challenging task in engineering domains. methodological approach applying MOO algorithms to simulation-enabled models has established itself as standard. Despite increasing computational power, the effectiveness and efficiency such algorithms, i.e., their ability identify many Pareto-optimal solutions possible with few simulati...

The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted the literature developed to simulate agent’s uncertain behaviors and transaction processes stochastically occurring dynamically changing ESC structures. These are important re...

We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have needed niching/sharing to ensure diversity. In this paper, we use a steady-state multiobjective algorithm which preserves diversity without niching to produce diverse sampling of the Pareto-front with significantly lower computati...

Abstract Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is common user deployment mode computing systems. In this mode, it has been challenge for systems to balance the quality of service revenue provider (CSP). To end, with multi-objective optimization (MOO) minimizing latency energy consumption, we propose TSRA framework based...