Tornado: An Autonomous Chaotic Algorithm for Large Scale Global Optimization

In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for large scale global optimization problems. The algorithm introduces advanced symmetrization, levelling and fine search strategies for an efficient and effective exploration of the search space and exploitation of the best found solutions. To our knowledge, this is the first accurate and fast autonomous chaotic algorithm solving large scale optimization problems. A panel of various benchmark problems with different properties were used to assess the performance of the proposed chaotic algorithm. The obtained results has shown the scalability of the algorithm in contrast to chaotic optimization algorithms encountered in the literature. Moreover, in comparison with some state-of-the-art meta-heuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm

Tornado: An Autonomous Chaotic Algorithm for Large Scale Global Optimization

In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for large scale global optimization problems. The algorithm introduces advanced symmetrization, levelling and fine search strategies for an efficient and effective exploration of the search space and exploitation of the best found solutions. To our knowledge, this is the first accurate and fast autonomous chaotic algorithm solving large scale optimization problems. A panel of various benchmark problems with different properties were used to assess the performance of the proposed chaotic algorithm. The obtained results has shown the scalability of the algorithm in contrast to chaotic optimization algorithms encountered in the literature. Moreover, in comparison with some state-of-the-art meta-heuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm

SPIDER: decomposition and path-relinking based algorithm for bi-objective optimization problems

This paper proposes an original bi-objective optimization approach around the key feature of local conservation of the Pareto stationnarity along the gradient axes (LCPS). The proposed algorithm consists of two steps. The decomposition step starts with the anchor points, generate N evenly points on the axes relating the anchor points to the utopia point. Then, the corresponding nearest reference points on the Pareto front are generated. In the path-relinking step, we carry out a path-relinking in the objective space, between each pair of Pareto solutions, following the best direction among the gradients axes. The SPIDER algorithm largely outperforms state-of-the-art and popular evolutionary algorithms both in terms of the quality of the obtained Pareto fronts (convergence, cardinality, diversity) and the search time

A New Chaotic-Based Approach for Multi-Objective Optimization

Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new approach based on Chaotic search to solve MOPs. Various Tchebychev scalarization strategies have been investigated. Moreover, a comparison with state of the art algorithms on different well known bound constrained benchmarks shows the efficiency and the effectiveness of the proposed Chaotic search approach

Tornado: An Autonomous Chaotic Algorithm for Large Scale Global Optimization

In this paper we propose an autonomous chaotic optimization algorithm, called Tornado, for large scale global optimization problems. The algorithm introduces advanced symmetrization, levelling and fine search strategies for an efficient and effective exploration of the search space and exploitation of the best found solutions. To our knowledge, this is the first accurate and fast autonomous chaotic algorithm solving large scale optimization problems. A panel of various benchmark problems with different properties were used to assess the performance of the proposed chaotic algorithm. The obtained results has shown the scalability of the algorithm in contrast to chaotic optimization algorithms encountered in the literature. Moreover, in comparison with some state-of-the-art meta-heuristics (e.g. evolutionary algorithms, swarm intelligence), the computational results revealed that the proposed Tornado algorithm is an effective and efficient optimization algorithm