The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, the author converts inequality constrained optimization problem into equality constrained optimization problem by using slack variables. Then we construct a new multiplier penalty function using the penalty function who belongs to equality constraints and was raised by Bertskas in 1982.
The social cognitive optimization algorithm is one of the newest intelligent algorithms, and this algorithm can help the solvers to avoid tripping in local optimization when solving the nonlinear constraint problems effectively. The algorithm is based on the social cognitive theory and the key point of the ergodicity is the process of refreshing the knowledge points. Modified and optimized the conditions...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.), which modify the shape of the search space. We propose an ecologically inspired invasive weed optimization (IWO) algorithm to solve the constrained real-parameter optimization problems. Central to our approach is a parameter-free penalty function that we introduce. The adaptive nature of the penalty...
This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high...
In this paper, the authors propose a new evolutionary optimization technique i.e. modified biogeography-based optimization (MBBO). This technique is an improved version of BBO with each solution is directly encoded by floating point. BBO is a new bio-inspired and population based optimization algorithm for global optimization. The exploitation ability of BBO method is good but it lacks in exploration...
This paper establishes a framework for designing fast, robust, and distributed algorithms for solving network utility maximization problems with coupled objective functions. We use two case studies in wireless communications to illustrate the key ideas: reverse-engineering the algorithm based on the KKT conditions of the optimization problem, and proving the properties of the algorithms using monotone...
When rate expectations of users in a wireless network cannot all be satisfied, one choice is to discard some users from the system, in a mechanism called admission control. However, in a data network, users have a certain tolerance to occasional rate outages. In this paper we argue that it may be preferable for users to reduce their rate objectives smoothly, by considering an outage probability tolerance,...
A space mapping algorithm with improved convergence properties for microwave design optimization is presented. In contrast to the previously published technique, a new convergence control method can be applied to surrogate models using non-extractable parameters, in particular, the output space mapping one of the most useful approaches to date. We demonstrate that the new algorithm allows for faster...
In this paper we present a centralized flow control scheme in NoCs in the presence of both elastic and streaming flow traffic paradigms. We model the desired best effort (BE) source rates as the solution to an alpha-fair utility maximization problem which is constrained with link capacities while preserving guaranteed service (GS) traffic requirements at the desired level. We propose an iterative...
A new polymorphic ant colony algorithm with weight is presented in order to make balance between accelerating convergence and averting precocity stagnation as well. We add weight to the initialization of pheromone and the choice of transition probability. The pheromone has a max-value and we choose the traditional method to update the pheromone. The simulation result from TSP problem shows the validity...
An open-closed-loop second-order iterative learning control algorithm is investigated in the presence of the system uncertainties. Through the rigorous analysis, it is obtained that this algorithm speeds up the convergence rate of iterative learning control in the iteration domain because the closed-loop learning gain is introduced based on an open-loop iterative learning control algorithm. It is...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.