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In this work a new improved version of Teatching Learnning Based Optimization algorithm, TLBO, is proposed. The new strategy is obtained by tne inclusion of the Bat Algorithm, BA, random local search part in the optimization process with TLBO algorithm. The developped hybrid algorihm is applied jointly with 2D non-linear finite elment method to solv the Team workshop problem 25.
The cooperative co-evolution (CC) framework is one of the most efficient methods to solve large scale optimization problems. The traditional CC framework divides decision variables into several mutually-exclusive groups. In this paper, we propose the overlapped cooperative co-evolution (OCC) framework for large scale optimization problems. In OCC framework, the decision variables that have strong...
In a game it is often the case that there are multiple roles or types of actors with different goals. One possible target for automatic content generation is to create multiple different software agents for these distinct roles. This paper outlines a technique, based on the multiple worlds model, for creating such actors via evolution. The objective function is based on the performance of the actors...
The idea of decomposition is becoming increasingly successful and popular in evolutionary multi-objective optimization. An efficient cone decomposition approach was further developed in the conical area evolutionary algorithm (CAEA). This approach improves the runtime efficiency and population diversity of decomposition-based algorithms effectively for bi-objective optimization in practice. In this...
In most differential evolution (DE) algorithms, little work for the design of the mutation operator is directly relevant to the information of fitness landscape of the problem being solved. As the previous studies show, different mutation strategies are suitable for different problems with different fitness landscapes, and the performance of the mutation strategy is tightly linked to the fitness landscape...
We present an arbitrary-shaped patch antenna design procedure. The design technique is based on a variant of Biogeography Based Optimization (BBO) algorithm. In this paper, we apply Opposition-Based Learning (OBL) concepts for antenna design. More specifically, we use a new Modified Biogeography Based Optimization (BBO) algorithm enhanced with OBL techniques. The preliminary results of the proposed...
Gravitational search algorithm is a nature inspired optimization algorithm, inspired by newton's law of gravity and law of motion. In this paper, a new variant of Gravitational search algorithm is presented. The exploration and exploitation capability of GSA is balanced by splitting the whole swarm into two groups. The search process is modified so that one group better exploits and one group becomes...
This paper presents an assessment of different computational intelligences, i.e evolutionary algorithm (EP), firefly algorithm (FA) and cuckoo search algorithm (CSA) for solving single-objective optimization problem. Recently, these algorithms have been widely used and applied to solve different types of optimization problems. However, the performance of these optimization algorithms have not been...
This work focus on the application of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) applied for the synchronization of two chaotic systems. Therefore, the synchronization problem is considered as an optimization problem. The GA and PSO approaches are used to minimize the synchronization error between the master and the slave chaotic systems. A comparative study between the GA and the...
This work aims to minimize average delay for an urban signalized intersection under oversaturated condition using genetic algorithm (GA). Relieving urban traffic congestion is an urgent call for traffic engineering. The effectiveness of traffic signalization is one of the key solutions to reduce congestion, but regrettably the current traffic signal control system is not fully optimized for handling...
In this paper, we proposed a Competitive QUasi-Affine TRansformation Evolutionary (C-QUATRE) algorithm. This algorithm is an advancement of a preciously proposed QUATRE algorithm. The QUATRE algorithm is arguably a very powerful stochastic optimization algorithm, and it will appear in CEC2016 conference proceedings with the paper title “QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm:...
Recently a number of evolutionary multi-objective optimization (EMO) algorithms have been proposed using the framework of MOEA/D (multi-objective evolutionary algorithm based on decomposition). Those algorithms are characterized by the use of uniformly distributed normalized weight vectors from which a set of uniformly distributed reference lines is generated. Their basic idea is to search for a Pareto...
The k-nearest neighbor (k-NN) algorithm is one of the most well-known supervised classifiers due to its ease of use and good performance. However, in spite of its popularity, k-NN suffers from some drawbacks such as high computational complexity, high storage requirements, and low noise tolerance. Prototype selection is a successful technique aimed at addressing aforementioned issues by reducing the...
Recent efforts in the evolutionary multi-objective optimization (EMO) community focus on addressing shortcomings of current solution techniques adopted for solving many-objective optimization problems (MaOPs). One such challenge faced by classical multi-objective evolutionary algorithms is diversity preservation in optimization problems with more than three objectives, namely MaOPs. In this vein,...
In an online learning environment where instructor-student interaction is limited video feedback may be an option to increase efficiency and value in the feedback process. Utilizing methods that decrease transactional distance for students in an online learning environment may also improve the overall learning experience. This proposal describes research intended to evaluate the experiences of both...
In this paper, an attempt has been made to develop an improved PSO based on Initial selection of Particles (ISBPSO) by selecting a better population of particles from the initially generated particles and this population has been generated based on function value. ISBPSO has been implemented to perform Economic load Dispatch on IEEE 5,14, and 30 bus systems and its performance has been compared to...
Biogeography Based Optimization (BBO) algorithm is a population based evolutionary optimization algorithm modeled on the theory of biogeography. Like other evolutionary algorithms, BBO also suffers from the problem of slow convergence. To improve the convergence property of the algorithm, global best solution inspired search strategy is incorporated with BBO. The modified strategy is named as global-best...
It is applicable to note that, in the overall wireless communication mechanism, the role played by the antenna is beyond the realm of words. In fact, the deployment of the antenna in wireless network faces the grave challenges of the communication of the high speed signals. Of late, the Nano-antennas make their elegant presence in several spheres of the technology. However, it is highly essential,...
The generalized quadratic assignment problem (GQAP) concerns with assigning m facilities to n locations. Multiple facilities can be assigned to a single location as long as the space allows. The aim is to find a way of assignment such that the total cost involved is minimized while respecting the space constraints. The total cost composes of installation and transportation costs, which is obtained...
Differential Evolution (DE), an optimization algorithm under the roof of Evolutionary Algorithms (EAs), is well known for its efficiency in solving optimization problems which are non-linear and non-differentiable. DE has many good qualities such as algorithmic simplicity, robustness and reliability. DE also has the quality of solving the given problem with few control parameters (NP — population...
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