Jing J. Liang

A self-adaptive global best harmony search algorithm for continuous optimization problems

This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm, a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth (BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants.

Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization

In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarmspsila size is very small. The whole population is divided into a large number sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. The Quasi-Newton method is combined to improve its local searching ability.

Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems

This article presents an approach to integrate a Pareto dominance concept into a comprehensive learning particle swarm optimizer (CLPSO) to handle multiple objective optimization problems. The multiobjective comprehensive learning particle swarm optimizer (MOCLPSO) also integrates an external archive technique. Simulation results (obtained using the codes made available on the Web at http://www.ntu.edu.sg/home/EPNSugan) on six test problems show that the proposed MOCLPSO, for most problems, is able to find a much better spread of solutions and faster convergence to the true Pareto‐optimal front compared to two other multiobjective optimization evolutionary algorithms.

Dynamic Multi-Swarm Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism

In this paper, a novel constraint-handling mechanism based on multi-swarm is proposed. Different from the existing constraints handling methods, the sub-swarms are adaptively assigned to explore different constraints according to their difficulties. The new mechanism is combined in dynamic multi-swarm optimizer (DMS-PSO) for handling constrained real-parameter optimization problems and sequential quadratic programming (SQP) method is combined to improve its local search ability. The performance of the modified DMS-PSO on the set of benchmark functions provided by CEC2006 [1] is reported.

Niching particle swarm optimization with local search for multi-modal optimization

Multimodal optimization is still one of the most challenging tasks for evolutionary computation. In recent years, many evolutionary multi-modal optimization algorithms have been developed. All these algorithms must tackle two issues in order to successfully solve a multi-modal problem: how to identify multiple global/local optima and how to maintain the identified optima till the end of the search. For most of the multi-modal optimization algorithms, the fine-local search capabilities are not effective. If the required accuracy is high, these algorithms fail to find the desired optima even after converging near them. To overcome this problem, this paper integrates a novel local search technique with some existing PSO based multimodal optimization algorithms to enhance their local search ability. The algorithms are tested on 14 commonly used multi-modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations.

A local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem

In this paper, a local-best harmony search (HS) algorithm with dynamic sub-harmony memories (HM), namely DLHS algorithm, is proposed to minimize the total weighted earliness and tardiness penalties for a lot-streaming flow shop scheduling problem with equal-size sub-lots. First of all, to make the HS algorithm suitable for solving the problem considered, a rank-of-value (ROV) rule is applied to convert the continuous harmony vectors to discrete job sequences, and a net benefit of movement (NBM) heuristic is utilized to yield the optimal sub-lot allocations for the obtained job sequences. Secondly, an efficient initialization scheme based on the NEH variants is presented to construct an initial HM with certain quality and diversity. Thirdly, during the evolution process, the HM is dynamically divided into many small-sized sub-HMs which evolve independently so as to balance the fast convergence and large diversity. Fourthly, a new improvisation scheme is developed to well inherit good structures from the local-best harmony vector in the sub-HM. Meanwhile, a chaotic sequence to produce decision variables for harmony vectors and a mutation scheme are utilized to enhance the diversity of the HM. In addition, a simple but effective local search approach is presented and embedded in the DLHS algorithm to enhance the local searching ability. Computational experiments and comparisons show that the proposed DLHS algorithm generates better or competitive results than the existing hybrid genetic algorithm (HGA) and hybrid discrete particle swarm optimization (HDPSO) for the lot-streaming flow shop scheduling problem with total weighted earliness and tardiness criterion.

A local-best harmony search algorithm with dynamic subpopulations

This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants.

Coevolutionary Comprehensive Learning Particle Swarm Optimizer

In this paper, a Coevolutionary Comprehensive Learning Particle Optimizer (Co-CLPSO) is proposed for solving constrained real-parameter optimization problems. In this novel algorithm, a coevolutionary schedule and a novel constraint-handling mechanism are employed. Two swarms with different thresholds are constructed and they exchange information in the evolution process. Different with the existing constraints handling methods, the particles are adaptively assigned to explore different constraints according to their difficulties. These new mechanisms are combined in Comprehensive Learning Particle Swarm Optimizer (CLPSO) and Sequential Quadratic Programming (SQP) method is combined to improve its local search ability. The performance of the proposed Co-CLPSO on the set of benchmark functions provided by CEC2010 [1] is reported.

Novel benchmark functions for continuous multimodal optimization with comparative results

Abstract Multi-modal optimization is concerned with locating multiple optima in one single run. Finding multiple solutions to a multi-modal optimization problem is especially useful in engineering, as the best solution may not always be the best realizable due to various practical constraints. To compare the performances of multi-modal optimization algorithms, multi-modal benchmark problems are always required. In this paper, 15 novel scalable multi-modal and real parameter benchmark problems are proposed. Among these 15 problems, 8 are extended simple functions while the rest are composition functions. These functions coordinate rotation and shift operations to create linkage among different dimensions and to place the optima at different locations, respectively. Four typical niching algorithms are used to solve the proposed problems. As shown by the experimental results, the proposed problems are challenging to these four recent algorithms.

Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm

In recent years, renewable energy sources such as wind energy have been used as one of the most effective ways to reduce pollution emissions. In this paper, a summation based multi-objective differential evolution (SMODE) algorithm is used to optimize the economic emission dispatch problem with stochastic wind power. The Weibull probability distribution function is used to model the stochastic nature of the wind power and the uncertainty is treated as the system constraints with stochastic variables. The algorithm is integrated with the superiority of feasible solution constraint handling technique. To validate the effectiveness of the proposed method, the standard IEEE 30-bus 6-generator test system with wind power (with/without considering losses) is studied with fuel cost and emission as two conflicting objectives to be optimized at the same time. Besides, a larger 40-generator system with wind farms is also solved by the proposed method. The results generated by SMODE are compared with those obtained using NSGAII as well as a number of techniques reported in literature. The results reveal that SMODE generates superior and consistent solutions.