In this paper, we study robust resource allocation for the multi-user full-duplex (FD) multiple-input multiple-output (MIMO) communication systems. Particularly, we aim at minimizing uplink (UL) transmit power and downlink (DL) transmit power simultaneously while guaranteeing the quality of service (QoS) requirements regarding secure UL and DL communication, under the consideration of the imper...

In this paper a new approach is proposed for the adaptation of the simulated annealing search in the field of the Multi-Objective Optimization (MOO). This new approach is called Multi-Case Multi-Objective Simulated Annealing (MC-MOSA). It uses some basics of a well-known recent Multi-Objective Simulated Annealing proposed by Ulungu et al., which is referred in the literature as U-MOSA. However,...

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has ...

Many real-world applications require the optimization of multiple conflicting criteria. For example, in robot locomotion, we are interested is maximizing speed while minimizing energy consumption. Multi-objective Bayesian optimization (MOBO) methods, such as ParEGO [6], ExI [5] and SMS-EGO [8] make use of models to define the next experiment, i.e., select the next parameters for which the objec...

Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The...

Feedback controls are usually designed to achieve multiple and often conflicting performance goals. These incommensurable objectives can be found in both time and frequency domains. For instance, one may want to design a control system such that the closed-loop system response to a step input has a minimum percentage overshoot (Mp), peak time (tp), rise time (tr), settling time (ts), tracking e...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...

This paper proposes the Multi Objective Optimization (MOO) of Vehicle Active Suspension System (VASS) with a hybrid Differential Evolution (DE) based Biogeography-Based Optimization (BBO) (DEBBO) for the parameter tuning of Proportional Integral Derivative (PID) controller. Initially a conventional PID controller, secondly a BBO, an rising nature enthused global optimization procedure based on ...

Designing supervised learning systems is in general a multi-objective optimization problem. It requires finding appropriate trade-offs between several objectives, for example between model complexity and accuracy or sensitivity and specificity. We consider the adaptation of kernel and regularization parameters of support vector machines (SVMs) by means of multi-objective evolutionary optimizati...

Clustering the results of a search can help a multi-document summarizer present a summary for evidence based medicine (EBM). In this work, we introduce a clustering technique that is based on multiobjective (MOO) optimization. MOO is a technique that shows promise in the areas of machine learning and natural language processing. In our approach we show how MOO based semi-supervised clustering t...