Jixiang Cheng

A hybrid approach based on differential evolution and tissue membrane systems for solving constrained manufacturing parameter optimization problems

This paper presents a hybrid approach based on appropriately combining Differential Evolution algorithms and Tissue P Systems (DETPS for short), used for solving a class of constrained manufacturing parameter optimization problems. DETPS uses a network membrane structure, evolution and communication rules like in a tissue P system to specify five widely used DE variants respectively put inside five cells of the tissue membrane system. Each DE variant independently evolves in a cell according to its own evolutionary mechanism and its parameters are dynamically adjusted in the process of evolution. DETPS applies the channels connecting the five cells of the tissue membrane system to implement communication in the process of evolution. Twenty-one benchmark problems taken from the specialized literature related to constrained manufacturing parameter optimization are used to test the DETPS performance. Experimental results show that DETPS is superior or competitive to twenty-two optimization algorithms recently reported in the literature.

Enhancing distributed differential evolution with multicultural migration for global numerical optimization

Differential evolution (DE) is a prominent stochastic optimization technique for global optimization. After its original definition in 1995, DE frameworks have been widely researched by computer scientists and practitioners. It is acknowledged that structuring a population is an efficient way to enhance the algorithmic performance of the original, single population (panmictic) DE. However, only a limited amount of work focused on Distributed DE (DDE) due to the difficulty of designing an appropriate migration strategy. Since a proper migration strategy has a major impact on the performance, there is a large margin of improvement for the DDE performance. In this paper, an enhanced DDE algorithm is proposed for global numerical optimization. The proposed algorithm, namely DDE with Multicultural Migration (DDEM) makes use of two migration selection approaches to maintain a high diversity in the subpopulations, Target Individual Based Migration Selection (TIBMS) and Representative Individual Based Migration Selection (RIBMS), respectively. In addition, the diversity amongst the individuals is controlled by means of the proposed Affinity Based Replacement Strategy (ABRS) mechanism. Numerical experiments have been performed on 34 diverse test problems. The comparisons have been made against DDE algorithms using classical migration strategies and three popular DDE variants. Experimental results show that DDEM displays a better or equal performance with respect to its competitors in terms of the quality of solutions, convergence, and statistical tests.

A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections

Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their performances deteriorate severely when handling many-objective optimization problems (MaOPs) due to the loss of selection pressure to drive the search toward the Pareto front and the ineffective design in diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity (DD) and favorable convergence (FC). The main features are the enhancement of two selection schemes to facilitate both convergence and diversity. In the algorithm, a mating selection based on FC is applied to strengthen selection pressure while an environmental selection based on DD and FC is designed to balance diversity and convergence. The proposed algorithm is tested on 64 instances of 16 MaOPs with diverse characteristics and compared with seven state-of-the-art algorithms. Experimental results show that the proposed MaOEA performs competitively with respect to chosen state-of-the-art designs.

Super-fit Multicriteria Adaptive Differential Evolution

This paper proposes an algorithm to solve the CEC2013 benchmark. The algorithm, namely Super-fit Multicriteria Adaptive Differential Evolution (SMADE), is a Memetic Computing approach based on the hybridization of two algorithmic schemes according to a super-fit memetic logic. More specifically, the Covariance Matrix Adaptive Evolution Strategy (CMAES), run at the beginning of the optimization process, is used to generate a solution with a high quality. This solution is then injected into the population of a modified Differential Evolution, namely Multicriteria Adaptive Differential Evolution (MADE). The improved solution is super-fit as it supposedly exhibits a performance a way higher than the other population individuals. The super-fit individual then leads the search of the MADE scheme towards the optimum. Unimodal or mildly multimodal problems, even when non-separable and ill-conditioned, tend to be solved during the early stages of the optimization by the CMAES. Highly multi-modal optimization problems are efficiently tackled by SMADE since the MADE algorithm (as well as other Differential Evolution schemes) appears to work very well when the search is led by a super-fit individual.

Multi-objective ant colony optimization based on decomposition for bi-objective traveling salesman problems

This paper proposes a framework named multi-objective ant colony optimization based on decomposition (MoACO/D) to solve bi-objective traveling salesman problems (bTSPs). In the framework, a bTSP is first decomposed into a number of scalar optimization subproblems using Tchebycheff approach. To suit for decomposition, an ant colony is divided into many subcolonies in an overlapped manner, each of which is for one subproblem. Then each subcolony independently optimizes its corresponding subproblem using single-objective ant colony optimization algorithm and all subcolonies simultaneously work. During the iteration, each subproblem maintains an aggregated pheromone trail and an aggregated heuristic matrix. Each subcolony uses the information to solve its corresponding subproblem. After an iteration, a pheromone trail share procedure is evoked to realize the information share of those subproblems solved by common ants. Three MoACO algorithms designed by, respectively, combining MoACO/D with AS, MMAS and ACS are presented. Extensive experiments conducted on ten bTSPs with various complexities manifest that MoACO/D is both efficient and effective for solving bTSPs and the ACS version of MoACO/D outperforms three well-known MoACO algorithms on large bTSPs according to several performance measures and median attainment surfaces.

Multicriteria adaptive differential evolution for global numerical optimization

Differential evolution DE has become a prevalent tool for global optimization problems since it was proposed in 1995. As usual, when applying DE to a specific problem, determining the most proper strategy and its associated parameter values is time-consuming. Moreover, to achieve good performance, DE often requires different strategies combined with different parameter values at different evolution stages. Thus integrating several strategies in one algorithm and determining the application rate of each strategy as well as its associated parameter values online become an ad-hoc research topic. This paper proposes a novel DE algorithm, called multicriteria adaptive DE MADE, for global numerical optimization. In MADE, a multicriteria adaptation scheme is introduced to determine the trial vector generation strategies and the control parameters of each strategy are separately adjusted according to their most recently successful values. In the multicriteria adaptation scheme, the impacts of an operator application are measured in terms of exploitation and exploration capabilities and correspondingly a multi-objective decision procedure is introduced to aggregate the impacts. Thirty-eight scale numerical optimization problems with various characteristics and two real-world problems are applied to test the proposed idea. Results show that MADE is superior or competitive to six well-known DE variants in terms of solution quality and convergence performance.

A particle swarm optimization based on P systems

This paper presents a novel membrane algorithm, called particle swarm optimization based on P systems (PSOPS), which combines P systems and particle swarm optimization. The PSOPS uses the representation of individuals, evolutionary rules of particle swarm optimization, and a hierarchical membrane structure and transformation or communication-like rules in P systems to design its algorithm. Experiments conducted on seven bench function optimization problems and time-frequency atom decomposition demonstrate the effectiveness and practicality of the introduced method.

Comparisons of quantum rotation gates in quantum-inspired evolutionary algorithms

As a novel evolutionary algorithm, a quantum-inspired evolutionary algorithm (QIEA) is attracting increasing attention, due to its good global search capability and rapid convergence. A quantum rotation gate (QR-gate) is the key operator in a QIEA. In the literature, there are many versions of QR-gates. How to evaluate and choose a QR-gate is very worth discussing. This paper focuses on a comparison and analysis on six QR-gates. The performances of QIEAs with the six QR-gates are tested on a practical problem, image sparse decomposition. Experimental results show that the QR-gate5 is superior to other five versions of QR-gates in terms of the quality of solutions and computing time.

A many-objective evolutionary algorithm based on directional diversity and favorable convergence

The performances of Pareto-based multi-objective evolutionary algorithms deteriorate severely when solving many-objective optimization problems (MaOPs) mainly due to the loss of selection pressure and inappropriate design in diversity maintenance mechanism. To handling MaOPs, this paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity and favorable convergence (MaOEA-DDFC). In the algorithm, the mating selection based on favorable convergence and Pareto-dominance is applied to strengthen the selection pressure while an environmental selection considering directional diversity and favorable convergence is designed in order to make a good trade-off between diversity and convergence. To validate algorithm performance, seven DTLZ problems with 3, 5, 7 and 10 objectives are tested. Experimental results show that the proposed MaOEA-DDFC performs better than five state-of-the-art MaOEAs in terms of inverted generational distance and hypervolume indicators.

Radar Emitter Signal Analysis with Estimation of Distribution Algorithms

This paper proposes a novel approach (short for iEDA/TFAD) based on estimation of distribution algorithms and time-frequency atom decomposition for analyzing radar emitter signals. In iEDA/TFAD, an improved estimation of distribution algorithm combining Gaussian and Cauchy probability models is presented to implement time-frequency atom decomposition to analyze several typical radar emitter signals by extracting their features and recognizing them. The introduction of iEDA can greatly reduce the computational complexity of TFAD. Experimental results show that EDA/TFAD can efficiently recognize several radar emitter signals at a high correct rate.