Genke Yang

Multiobjective optimization using population-based extremal optimization

In recent years, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied in some NP-hard combinatorial optimization problems. In this paper, we present a novel Pareto-based algorithm, which can be regarded as an extension of EO, to solve multiobjective optimization problems. The proposed method, called Multiobjective Population-based Extremal Optimization (MOPEO), is validated by using five benchmark functions and metrics taken from the standard literature on multiobjective evolutionary optimization. The experimental results demonstrate that MOPEO is competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOPEO can be considered as a viable alternative to solve multiobjective optimization problems.

Production scheduling optimization algorithm for the hot rolling processes

The hot rolling production scheduling problem is an extremely difficult and time-consuming process, so it is quite difficult to achieve an optimal solution with traditional optimization methods owing to the high computational complexity. To ensure the feasibility of solutions and improve the efficiency of the scheduling, this paper proposes a vehicle routing problem (VRP) to model the problem and develops an easily implemented hybrid approach (QPSO-SA) to solve the problem. In the hybrid approach, quantum particle swarm optimization (QPSO) combines local search and global search to search the optimal results and simulated annealing (SA) employs certain probability to avoid getting into a local optimum. The computational results from actual production data have shown that the proposed model and algorithm are feasible and effective for the hot rolling scheduling problem.

Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization

Recently, a local-search heuristic algorithm called extremal optimization (EO) has been successfully applied in some combinatorial optimization problems. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Levy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular benchmark problems, it has been shown that our approach is a good alternative to deal with the numerical constrained optimization problems

A spectral clustering-based framework for detecting community structures in complex networks

Abstract Exploring recent developments in spectral clustering, we discovered that relaxing a spectral reformulation of Newman’s Q -measure (a measure that may guide the search for–and help to evaluate the fit of - community structures in networks) yields a new framework for use in detecting fuzzy communities and identifying so-called unstable nodes. In this note, we present and illustrate this approach, which we expect to further enhance our understanding of the intrinsic structure of networks and of network-based clustering procedures. We applied a variation of the fuzzy k -means algorithm, an instance of our framework, to two social networks. The computational results illustrate its potential.

Design PID controllers for desired time-domain or frequency-domain response.

Practical requirements on the design of control systems, especially process control systems, are usually specified in terms of time-domain response, such as overshoot and rise time, or frequency-domain response, such as resonance peak and stability margin. Although numerous methods have been developed for the design of the proportional-integral-derivative (PID) controller, little work has been done in relation to the quantitative time-domain and frequency-domain responses. In this paper, we study the following problem: Given a nominal stable process with time delay, we design a suboptimal PID controller to achieve the required time-domain response or frequency-domain response for the nominal system or the uncertain system. An H(infinity) PID controller is developed based on optimal control theory and the parameters are derived analytically. Its properties are investigated and compared with that of two developed suboptimal controllers: an H2 PID controller and a Maclaurin PID controller. It is shown that all three controllers can provide the quantitative time-domain and frequency-domain responses.

A data-driven approximate causal inference model using the evidential reasoning rule

This paper aims to develop a data-driven approximate causal inference model using the newly-proposed evidential reasoning (ER) rule. The ER rule constitutes a generic conjunctive probabilistic reasoning process and generalises Dempsters rule and Bayesian inference. The belief rule based (BRB) methodology was developed to model complicated nonlinear causal relationships between antecedent attributes and consequents on the basis of the ER algorithm and traditional IF-THEN rule-based systems, and in essence it keeps methodological consistency with Bayesian Network (BN). In this paper, we firstly introduce the ER rule and then analyse its inference patterns with respect to the bounded sum of individual support and the orthogonal sum of collective support from multiple pieces of independent evidence. Furthermore, we propose an approximate causal inference model with the kernel mechanism of data-based approximate causal modelling and optimal learning. The exploratory approximate causal inference model inherits the main strengths of BN, BRB and relevant techniques, and can potentially extend the boundaries of applying approximate causal inference to complex decision and risk analysis, system identification, fault diagnosis, etc. A numerical study on the practical pipeline leak detection problem demonstrates the applicability and capability of the proposed data-driven approximate causal inference model.

A method of solving a large-scale rolling batch scheduling problem in steel production using a variant of column generation

This paper presents a transparent model and a solution approach to solve a large-scale rolling batch scheduling problem. First, the problem is formulated as a multiple routes problem with multi-objective (MRMOP). By defining a hierarchical cost structure it is natural to decompose the MRMOP into several well-studied sub-problems, i.e. the multiple routes minimum cost problem (MRMCP), the knapsack problem (KP) and the linear assignment problem (LAP). Among these sub-problems the MRMCP is considered as the central one and is tackled first of all. The solution procedure for the MRMCP is based on a partial set-partitioning formulation. It makes use of a variant of column generation. Feasible column is generated as needed by solving a resource constrained elementary shortest path problem (RC-ESPP) by a mixed strategy combing an exact method and heuristics. Then a procedure called Adding-Node is introduced to implicitly solve the KP starting from the solution of the MRMCP. Finally, we solve the LAP with Hungarian algorithm to consider the total tardiness and earliness of the production. Computational results are presented compared with several promising methods on benchmark problems and production orders from Shanghai Baoshan Iron and Steel Complex. The results demonstrate the efficiency of the proposed algorithm.

A parameter estimation method for biological systems modelled by ODE/DDE models using spline approximation and differential evolution algorithm

The inverse problem of identifying unknown parameters of known structure dynamical biological systems, which are modelled by ordinary differential equations or delay differential equations, from experimental data is treated in this paper. A two stage approach is adopted: first, combine spline theory and Nonlinear Programming (NLP), the parameter estimation problem is formulated as an optimization problem with only algebraic constraints; then, a new differential evolution (DE) algorithm is proposed to find a feasible solution. The approach is designed to handle problem of realistic size with noisy observation data. Three cases are studied to evaluate the performance of the proposed algorithm: two are based on benchmark models with priori-determined structure and parameters; the other one is a particular biological system with unknown model structure. In the last case, only a set of observation data available and in this case a nominal model is adopted for the identification. All the test systems were successfully identified by using a reasonable amount of experimental data within an acceptable computation time. Experimental evaluation reveals that the proposed method is capable of fast estimation on the unknown parameters with good precision.

A multi-objective optimisation algorithm for the hot rolling batch scheduling problem

The hot rolling batch scheduling problem is a hard problem in the steel industry. In this paper, the problem is formulated as a multi-objective prize collecting vehicle routing problem (PCVRP) model. In order to avoid the selection of weight coefficients encountered in single objective optimisation, a multi-objective optimisation algorithm based on Pareto-dominance is used to solve this model. Firstly, the Pareto ℳ𝒜𝒳–ℳℐ𝒩 Ant System (P-ℳℳAS), which is a brand new multi-objective ant colony optimisation algorithm, is proposed to minimise the penalties caused by jumps between adjacent slabs, and simultaneously maximise the prizes collected. Then a multi-objective decision-making approach based on TOPSIS is used to select a final rolling batch from the Pareto-optimal solutions provided by P-ℳℳAS. The experimental results using practical production data from Shanghai Baoshan Iron & Steel Co., Ltd. have indicated that the proposed model and algorithm are effective and efficient.

Extremal Optimization for protein folding simulations on the lattice

This paper presents a novel guided search strategy Extremal Optimization (EO) with constrained structure for protein folding. In the proposed algorithm, evaluating the fitness of each monomer in an amino-acid sequence is introduced to guide the improvement of the conformation. In addition, a constrained structure is proposed to reduce the complexity of algorithm. We demonstrate that EO can be applied successfully to the protein folding problem. The results show that the algorithm can find the best solutions so far for the listed benchmarks. Within the achieved results, the search converged rapidly and efficiently.