Dexuan Zou

A novel modified differential evolution algorithm for constrained optimization problems

A novel modified differential evolution algorithm (NMDE) is proposed to solve constrained optimization problems in this paper. The NMDE algorithm modifies scale factor and crossover rate using an adaptive strategy. For any solution, if it is at a standstill, its own scale factor and crossover rate will be adjusted in terms of the information of all successful solutions. We can obtain satisfactory feasible solutions for constrained optimization problems by combining the NMDE algorithm and a common penalty function method. Experimental results show that the proposed algorithm can yield better solutions than those reported in the literature for most problems, and it can be an efficient alternative to solving constrained optimization problems.

A modified differential evolution algorithm for unconstrained optimization problems

A modified differential evolution algorithm (MDE) is proposed to solve unconstrained optimization problems in this paper. Gauss distribution and uniform distribution have one thing in common, that is randomness or indeterminateness. Due to this characteristic, MDE employs both distributions to adjust scale factor and crossover rate, which is useful to increase the diversity of the entire population. To guarantee the quality of the swarm, MDE uses an external archive, and some solutions of high quality in this external archive can be selected for candidate solutions. MDE adopts two common mutation strategies to produce new solutions, and the information of global best solution is more likely to be utilized for the mutation during late evolution process, which is beneficial to improving the convergence of the proposed algorithm. In addition, a central solution is generated in terms of all the other candidate solutions, and it can provide a potential searing direction. Experimental results show that MDE algorithm can yield better objective function values than the other six DE algorithms for some unconstrained optimization problems, thus it is an efficient alternative on solving unconstrained optimization problems.

Brief paper: An improved differential evolution algorithm for the task assignment problem

An improved differential evolution algorithm (IDE) is proposed to solve task assignment problem. The IDE is an improved version of differential evolution algorithm (DE), and it modifies two important parameters of DE algorithm: scale factor and crossover rate. Specially, scale factor is adaptively adjusted According to the objective function values of all candidate solutions, and crossover rate is dynamically adjusted with the increasement of iterations. The adaptive scale factor and dynamical crossover rate are combined to increase the diversity of candidate solutions, and to enhance the exploration capacity of solution space of the proposed algorithm. In addition, a usual penalty function method is adopted to trade-off the objective and the constraints. Experimental results demonstrate that the optimal solutions obtained by the IDE algorithm are all better than those obtained by the other two DE algorithms on solving some task assignment problems.

Teaching-learning based optimization with global crossover for global optimization problems

Teaching learning based optimization (TLBO) is a newly developed population-based meta-heuristic algorithm. It has better global searching capability but it also easily got stuck on local optima when solving global optimization problems. This paper develops a new variant of TLBO, called teaching learning based optimization with global crossover (TLBO-GC), for improving the performance of TLBO. In teaching phase, a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima. And a new global crossover strategy is incorporated into the learning phase, which aims at balancing local and global searching effectively. The performance of TLBO-GC is assessed by solving global optimization functions with different characteristics. Compared to the TLBO, several modified TLBOs and other promising heuristic methods, numerical results reveal that the TLBO-GC has better optimization performance.

Hybrid harmony search particle swarm optimization with global dimension selection

This study presents a hybrid harmony search particle swarm optimization with global dimension selection (HHSPSO-GDS) for improving the performance of particle swarm optimization (PSO). In HHSPSO-GDS, a new global velocity updating strategy is introduced to enhance the neighborhood region search of the current best solution and to get a better trade-off between convergence rate and robustness. Additionally, a dynamic non-linear decreased inertia weight is utilized to balance the global exploration and local exploitation. Moreover, the best-worst improvisation mechanism of harmony search (HS) is implanted in the HHSPSO-GDS algorithm and a global dimension selection is employed in the improvisation process, which can effectively accelerate convergence. Global best information sharing strategy is developed to link the two layer exploration frames (PSO and HS). Finally, a comprehensive experimental study is conducted on a large number of benchmark functions. The experimental results reveal that HHSPSO-GDS performs better in terms of the quality of solution, convergence rate, robustness and scalability compared to various state-of-the-art PSOs and other meta-heuristic search algorithms.

On the iterative convergence of harmony search algorithm and a proposed modification

Inspired by the improvisation process of music players, a population-based meta-heuristic algorithm-harmony search (HS) has been proposed recently. HS is good at exploitation, but it can be poor at exploration, and its convergence performance can also be an issue in some cases. To address these disadvantages, the distance bandwidth (bw) adjusting methods proposed in recent literatures are summarized and the exploration ability of HS improvisation is investigated in this paper. Further, the relationship between improvisation exploration and each parameter under asymmetric interval is derived, and an iterative convergence sufficiency of the iteration equation which consists of variance expectation and mean expectation is proven theoretically. Based on these analyses, a modified harmony search (MHS) algorithm is proposed. Moreover, the effects of the key parameters including HMS, PAR and HMCR on the performance of the MHS algorithm are discussed in depth. Experimental results reveal that the proposed MHS algorithm performs better than HS as well as its state-of-the-art variants and other classic excellent meta-heuristic approaches.

Directed searching optimization algorithm for constrained optimization problems

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations - position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.

Improved Harmony Search Algorithm

Display OmittedThe improvisation process of LHS algorithm. Opposition-based learning (OBL) technique is employed in improvisation process. The purpose is to increase the diversity of solution.The current best harmony and worst harmony are used to adjust the parameter BW. An adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space.In the proposed algorithm, a new harmony and its opposite harmony are generated in iteration. Then a competition selection mechanism is established to improve solution precision.The effects that varying the parameter HMS and HMCR have on the performance of the LHS algorithm is also analyzed in detail. In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i) adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space; (ii) opposition-based learning technique is blended to increase the diversity of solution; (iii) competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima. The performance of the LHS algorithm with respect to harmony memory size (HMS) and harmony memory considering rate (HMCR) are also analyzed in detail. To further evaluate the performance of the proposed LHS algorithm, comparison with ten state-of-the-art harmony search variants over a large number of benchmark functions with different characteristics is carried out. The numerical results confirm the superiority of the proposed LHS algorithm in terms of accuracy, convergence speed and robustness.

Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

This paper presents a ranked differential evolution (RDE) algorithm for solving the identification problem of non-linear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.

A modified simulated annealing algorithm and an excessive area model for floorplanning using fixed-outline constraints

Outline-free floorplanning focuses on area and wirelength reductions, which are usually meaningless, since they can hardly satisfy modern design requirements. We concentrate on a more difficult and useful issue, fixed-outline floorplanning. This issue imposes fixed-outline constraints on the outline-free floorplanning, making the physical design more interesting and challenging. The contributions of this paper are primarily twofold. First, a modified simulated annealing (MSA) algorithm is proposed. In the beginning of the evolutionary process, a new attenuation formula is used to decrease the temperature slowly, to enhance MSA’s global searching capacity. After a period of time, the traditional attenuation formula is employed to decrease the temperature rapidly, to maintain MSA’s local searching capacity. Second, an excessive area model is designed to guide MSA to find feasible solutions readily. This can save much time for refining feasible solutions. Additionally, B*-tree representation is known as a very useful method for characterizing floorplanning. Therefore, it is employed to perform a perturbing operation for MSA. Finally, six groups of benchmark instances with different dead spaces and aspect ratios—circuits n10, n30, n50, n100, n200, and n300—are chosen to demonstrate the efficiency of our proposed method on fixed-outline floorplanning. Compared to several existing methods, the proposed method is more efficient in obtaining desirable objective function values associated with the chip area, wirelength, and fixed-outline constraints.