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In order to overcome the drawbacks of low computational accuracy and slow convergence rate of conventional artificial bee colony (ABC) algorithm during solving the function optimization problems, this paper proposed a self-adaptive ABC algorithm with enhanced search ability. The modified algorithm used the logic self-mapping function to generate chaotic sequences so as to implement chaos optimization for the search behaviors of employed bees, and it adopted adaptive step strategy of firefly algorithm to dynamically adjust the search behaviors of observer bees, the approach improved the local search ability. Simulations based on standard testing functions indicate that the modified ABC algorithm exhibits obviously improved optimization accuracy and convergence rate. [ABSTRACT FROM AUTHOR]

The basic particle swarm (PSO)algorithm could not solve the traveling salesman problem (TSP) effectively. This paper analyzed the basic particle swarm optimization mechanism, and defined the new evolution equations in particle swarm optimization based evolutionary operators on the used of chaotic motion characteristics of randomness and ergodicity. It put forward a kind of improved chaotic particle with chaos optimization algorithm and particle swarm optimization algorithm. The algorithm was to adjust the inertia weight, introduced the chaotic carrier adjustment of the search strategy to avoid falling into local optimum, formed both global and local search hybrid discrete particle swarm algorithm, made it suitable for solving TSP combinatorial optimization problems. The algorithm was simulated by MATLAB. The results show that the precision of the algorithm, the convergence speed and optimization efficiency is better, also show that this algorithm is effectively in TSP application. It provides a reference method for solving the TSP problem. [ABSTRACT FROM AUTHOR]