Jianke Zhang

The KKT optimality conditions in a class of generalized convex optimization problems with an interval-valued objective function

In this paper, we study the Karush–Kuhn–Tucker optimality conditions in a class of nonconvex optimization problems with an interval-valued objective function. Firstly, the concepts of preinvexity and invexity are extended to interval-valued functions. Secondly, several properties of interval-valued preinvex and invex functions are investigated. Thirdly, the KKT optimality conditions are derived for LU-preinvex and invex optimization problems with an interval-valued objective function under the conditions of weakly continuous differentiablity and Hukuhara differentiablity. Finally, the relationships between a class of variational-like inequalities and the interval-valued optimization problems are established.

Biogeography-based optimization with improved migration operator and self-adaptive clear duplicate operator

Biogeography-based optimization (BBO) is a new emerging population-based algorithm that has been shown to be competitive with other evolutionary algorithms. However, there are some insufficiencies in solving complex problems, such as poor population diversity and slow convergence speed in the later stage. To overcome these shortcomings, we propose an improved BBO (IBBO) algorithm integrating a new improved migration operator, Gaussian mutation operator, and self-adaptive clear duplicate operator. The improved migration operator simultaneously adopts more information from other habitats, maintains population diversity, and preserves exploitation ability. The self-adaptive clear duplicate operator can clear duplicate or almost identical habitats, while also preserving population diversity through a self-adaptation threshold within the evolution process. Simulation results and comparisons from the experimental tests conducted on 23 benchmark functions show that IBBO achieves excellent performance in solving complex problems compared with other variants of the BBO algorithm and other evolutionary algorithms. The performance of the improved migration operator is also discussed.

Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm

Biogeography-based optimization algorithm (BBO) is a relatively new optimization technique which has been shown to be competitive to other biology-based algorithms. However, there is still an insufficiency in BBO regarding its migration operator, which is good at exploitation but poor at exploration. To address this concerning issue, we propose an improved BBO (IBBO) by using a modified search strategy to generate a new mutation operator so that the exploration and exploitation can be well balanced and then satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, both opposition-based learning methods and chaotic maps are employed, when producing the initial population. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as high-dimension numerical optimization problems with multiple local optima. Numerical simulations and comparisons with some typical existing algorithms demonstrate the effectiveness and efficiency of the proposed approach.

Biogeography-Based Optimization with Orthogonal Crossover

Biogeography-based optimization (BBO) is a new biogeography inspired, population-based algorithm, which mainly uses migration operator to share information among solutions. Similar to crossover operator in genetic algorithm, migration operator is a probabilistic operator and only generates the vertex of a hyperrectangle defined by the emigration and immigration vectors. Therefore, the exploration ability of BBO may be limited. Orthogonal crossover operator with quantization technique (QOX) is based on orthogonal design and can generate representative solution in solution space. In this paper, a BBO variant is presented through embedding the QOX operator in BBO algorithm. Additionally, a modified migration equation is used to improve the population diversity. Several experiments are conducted on 23 benchmark functions. Experimental results show that the proposed algorithm is capable of locating the optimal or closed-to-optimal solution. Comparisons with other variants of BBO algorithms and state-of-the-art orthogonal-based evolutionary algorithms demonstrate that our proposed algorithm possesses faster global convergence rate, high-precision solution, and stronger robustness. Finally, the analysis result of the performance of QOX indicates that QOX plays a key role in the proposed algorithm.

On fuzzy generalized convex mappings and optimality conditions for fuzzy weakly univex mappings

In this paper, we first introduce the weakly invex fuzzy mappings based on weakly differentiable functions and discuss the relationships between several kinds of fuzzy generalized convex mappings. Then a kind of fuzzy weakly univex functions are introduced and some properties of them are investigated. Furthermore, optimality and duality results are derived for a class of generalized convex optimization problems with fuzzy weakly univex functions.

Univex Interval-Valued Mapping with Differentiability and Its Application in Nonlinear Programming

Interval-valued univex functions are introduced for differentiable programming problems. Optimality and duality results are derived for a class of generalized convex optimization problems with interval-valued univex functions.

Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

Harmony search (HS) method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE) is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

On interval-valued invex mappings and optimality conditions for interval-valued optimization problems

*Correspondence: lilifeng80@163.com 1School of Science, Xi’an University of Posts and Telecommunications, Xi’an, China Full list of author information is available at the end of the article Abstract In this paper, we first introduce the concept of interval-valued invex mappings by using gH-differentiability and compare it with interval-valued weakly invex mappings. We can observe that interval-valued invex mappings are more general than interval-valued weakly invex mappings. In addition, the sufficient optimality condition for interval-valued objective functions is derived under invexity.

Sufficiency and duality for multiobjective variational control problems with G-invexity

Antczak introduced vector-valued G-invex functions in 2009, which is a new class of generalized convex functions for differentiable multiobjective programming problems. In this paper, we extend the vector-valued G-invex functions to multiobjective variational control problems. By using the new concepts, a number of sufficient optimality results and Mond-Weir type duality results are obtained for multiobjective variational control programming problems.