Changsheng Zhang

A best firework updating information guided adaptive fireworks algorithm

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) has significant performance on solving single objective problems, and has been applied broadly on a number of fields. To further improve its performance, a best firework updating information guided adaptive fireworks algorithm (PgAFWA) is proposed, in which the evolving process is guided by the direction from previous best firework to the current best firework from two aspects: amplifying the explosion amplitude on the direction that the best firework is updated, and making more sparks which are generated by the best firework distributed on this direction to further enhance the exploring ability on it. Numerical experiment on CEC2015 test suite was implemented to verify performance of the proposed algorithm. The experiment results indicated that the PgAFWA outperformed the compared algorithms in terms of both convergence speed and solving quality.

Ant-colony algorithm with a strengthened negative-feedback mechanism for constraint-satisfaction problems

Describing the definition and usage of negative feedback in ant colony optimization.Proposing a new negative pheromone matrix and combined with positive pheromone matrix in probability formula.A novel strengthening negative feedback mechanism is proposed to improve efficiency and avoid local optimal.Detail experiment design has been given for evaluating the proposed approaches based on different standard real datasets and synthetically generated datasets. Ant Colony Optimization (ACO) is an efficient way to solve binary constraint-satisfaction problems (CSPs). In recent years, new improvements have only considered enhancing the positive feedback to increase the convergence speed. However, through the study and analysis of these enhanced ACO algorithms, we determined that they still suffer from the problem of easily getting in locally optimal solutions. Thus, an improved ACO algorithm with a strengthened negative-feedback mechanism is designed to tackle CSPs. This new algorithm takes advantage of search-history information and continually obtains failure experience to guide the ant swarm exploring the unknown space during the optimization process. The starting point of this algorithm is to utilize the negative feedback to improve the diversity of solutions. Finally, we use 24 CSP samples and 25 Queen samples to perform experiments, compare this algorithm with other related algorithms and conduct performance assessment. The preliminary results show that ACO with negative feedback outperforms the compared algorithms in identifying high-quality solutions.

STARIMA-based traffic prediction with time-varying lags

Based on the observation that the correlation between observed traffic at two measurement points or traffic stations may be time-varying, attributable to the time-varying speed which subsequently causes variations in the time required to travel between the two points, in this paper, we develop a modified Space-Time Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags for short-term traffic flow prediction. Particularly, the temporal lags in the modified STARIMA change with the time-varying speed at different time of the day or equivalently change with the (time-varying) time required to travel between two measurement points. Firstly, a technique is developed to evaluate the temporal lag in the STARIMA model, where the temporal lag is formulated as a function of the spatial lag (spatial distance) and the average speed. Secondly, an unsupervised classification algorithm based on ISODATA algorithm is designed to classify different time periods of the day according to the variation of the speed. The classification helps to determine the appropriate time lag to use in the STARIMA model. Finally, a STARIMA-based model with time-varying lags is developed for short-term traffic prediction. Experimental results using real traffic data show that the developed STARIMA-based model with time-varying lags has superior accuracy compared with its counterpart developed using the traditional cross-correlation function and without employing time-varying lags.

A food source-updating information-guided artificial bee colony algorithm

Artificial bee colony algorithm simulates the foraging behavior of honey bees, which has shown good performance in many application problems and large-scale optimization problems. To model the bees foraging behavior more accurately, a food source-updating information-guided artificial bee colony algorithm is proposed in this paper. In this algorithm, some food source-updating information obtained during optimizing time is introduced to redefine the foraging strategies of artificial bees. The proposed algorithm has been tested on a set of test functions with dimension 30, 100, 1000 and compared with some recently proposed related algorithms. The experimental results show that the performance of artificial bee colony algorithm is significantly improved for both rotated problems and large-scale problems. Compared with the related algorithms, the proposed algorithm can achieve better or competitive performance on most test functions and greatly better performance on parts of test functions.

Decomposition-based sub-problem optimal solution updating direction-guided evolutionary many-objective algorithm

Abstract The many-objective optimization problem (MaOP) is a common problem in the fields of engineering and scientific computing. It requires the optimization of multiple conflicting objectives. Due to the complexity of the MaOP, its optimization requires considerable amounts of time and computation resources to execute. Moreover, demand for a general optimization method for different types of MaOPs is becoming increasingly urgent. In this paper, the reference-vector-guided evolutionary algorithm (RVEA) is modified to accelerate the optimization speed and to improve its adaptability. To achieve more rapid convergence, a sub-problem optimal solution updating direction-guided variation strategy is developed to replace the original variation strategy of the RVEA. A comparative experiment on the typical test suites verifies that the proposed method offers preferable performance. Our experiment shows that the performance of the OD-RVEA declines when optimizing MaOPs with irregular Pareto fronts (PFs). To address this issue, an adaptive reference vector adjustment strategy is designed as a means of enhancing the optimization capabilities of MaOPs with irregular PFs by adjusting the distribution of reference vectors. Our comparative experiment on test cases that involve irregular PFs shows that the algorithm that applies this strategy outperforms the algorithm that applies fixed reference vectors.

Artificial bee colony algorithm with strategy and parameter adaptation for global optimization

The artificial bee colony (ABC) algorithm has been successfully applied to solve a wide range of real-world optimization problems. However, the success of ABC in solving a specific problem crucially depends on appropriately choosing the foraging strategies and its associated parameters. In this paper, we propose a strategy and parameter self-adaptive selection ABC algorithm (SPaABC), in which both employed bees search strategies and their associated control parameter values are gradually self-adaptive by learning from their previous experiences in generating promising solutions. In order to verify the performance of our approach, SPaABC algorithm is compared to many recently related algorithms on eighteen benchmark functions. Experimental results indicate that the proposed algorithm achieves competitive performance on most test instances.

An improved ant colony optimization algorithm with strengthened pheromone updating mechanism for constraint satisfaction problem

Constraint satisfaction problem (CSP) is a fundamental problem in the field of constraint programming. To tackle this problem more efficiently, an improved ant colony optimization algorithm is proposed. In order to further improve the convergence speed under the premise of not influencing the quality of the solution, a novel strengthened pheromone updating mechanism is designed, which strengthens pheromone on the edge which had never appeared before, using the dynamic information in the process of the optimal path optimization. The improved algorithm is analyzed and tested on a set of CSP benchmark test cases. The experimental results show that the ant colony optimization algorithm with strengthened pheromone updating mechanism performs better than the compared algorithms both on the quality of solution obtained and on the convergence speed.

A multi-objective optimization method for service composition problem with sharing property

For service level agreement aware service composition problem, the sharing property is integrated into it to further decrease the cost and make more applications can be successfully deployed. Based on the characteristic of this problem, the attributes of throughput, latency and cost are simultaneous considered and a multi-objective service sharing optimization model is built. To make more feasible service composition candidates be included, a particle swarm optimization based multi-objective method is proposed to solve it. The results show that more feasible solutions with lower cost can be found when the resource sharing property is considered.

An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem

Research on multi-objective optimization (MO) has become one of the hot points of intelligent computation. In this paper, an archive-based multi-objective artificial bee colony optimization algorithm (AMOABC) is proposed, in which an external archive is used to preserve the current obtained non-dominated best solutions, and a novel Pareto local search mechanism is designed and incorporated into the optimization process. To prevent the searching process from being trapped into local minimum, a novel food source generating mechanism is put forward, and different search strategies are designed for bees and local search process. Comprehensive benchmarking and comparison of AMOABC with the some current-related MO algorithms demonstrate its effectiveness.

A best-path-updating information-guided ant colony optimization algorithm

Abstract The ant colony optimization (ACO) algorithm is a type of classical swarm intelligence algorithm that is especially suitable for combinatorial optimization problems. To further improve the convergence speed without affecting the solution quality, in this paper, a novel strengthened pheromone update mechanism is designed that strengthens the pheromone on the edges, which had never been done before, utilizing dynamic information to perform path optimization. In addition, to enhance the global search capability, a novel pheromone-smoothing mechanism is designed to reinitialize the pheromone matrix when the ACO algorithms search process approaches a defined stagnation state. The improved algorithm is analyzed and tested on a set of benchmark test cases. The experimental results show that the improved ant colony optimization algorithm performs better than compared algorithms in terms of both the diversity of the solutions obtained and convergence speed.