In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

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An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

Gradient-based optimizer: A new metaheuristic optimization algorithm

Variants of Newton’s Method using fifth-order quadrature formulas☆

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

We present a new modification of the Newton's method which produces iterative methods with order of convergence three. A general error analysis providing the higher order of convergence is given, and the best efficiency, in term of function evaluations, of two of this new methods is provided.

Some variant of newtons method with third-order convergence

A variant of Newton's method with accelerated third-order convergence

Most real world combinatorial optimization problems are difficult to solve with multiple objectives which have to be optimized simultaneously. Over the last few years, researches have been proposed several ant colony optimization algorithms to solve multiple objectives. The aim of this paper is to review the recently proposed multi-objective ant colony optimization (MOACO) algorithms and compare their performances on two, three and four objectives with different numbers of ants and numbers of iterations. Moreover, a detailed analysis is performed for these MOACO algorithms by applying them on several multi-objective benchmark instances of the traveling salesman problem. The results of the analysis have shown that most of the considered MOACO algorithms obtained better performances for more than two objectives and their performance depends slightly on the number of objectives, number of iterations and number of ants used.

Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem

The context of this work is the development of persons’ personality recognition system using machine learning techniques. Identifying the personality traits from a face image are helpful in many situations, such as identification of criminal behavior in criminology, students’ learning attitudes in education sector and recruiting employees. Identifying the personality traits from a face image has rarely been studied. In this research identifying the personality traits from a face image includes three separate methods; ANN, SVM and deep learning. Face area of an image is identified by a color segmentation algorithm. Then that extracted image is input to personality recognition process. Features of the face are identified manually and input them to ANN and SVM. Each personality trait is valued from 1 to 9. In the second attempt m-SVM is used because outputs are multi-valued. ANN gave better results than m-SVM. In the third attempt we propose a methodology to identify personality traits using deep learning.

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T. G. I. Fernando

Papers

A variant of Newton's method with accelerated third-order convergence

Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem

Persons’ Personality Traits Recognition using Machine Learning Algorithms and Image Processing Techniques

Algorithms for path optimizations: a short survey