Hierarchical particle swarm optimizer for minimizing the non-convex potential energy of molecular structure

The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular size. In this paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudo-ethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with p-value less than 0.01277 over molecular potential energy function.     

A negotiation support system for resolving an international trans-boundary natural resource conflict

Post-Soviet legal governance regime of Caspian Sea – the largest inland body of water on earth – remains a source of conflict among the five coastal states of Azerbaijan, Iran, Kazakhstan, Russia, and Turkmenistan. Although different division methods have been suggested for sharing the sea and its valuable resources, the actual gain of the countries is unclear as the proposed methods focus either on the oil and gas or the areal share of the parties. The Caspian Sea Negotiation Support System (Caspian Sea NSS) is developed in this study to delineate optimal boundaries for sharing the sea through simultaneous consideration of the countries' areal and resource shares under different sharing methods. This NSS is a complex optimization model, with a solver engine that provides reliable results with a reasonable computational effort using a heuristic method. The model is run under different division scenarios to evaluate the sensitivity of each party's gain and locations of nautical boundaries to the division rules and the economic values of the resources. Results show a high sensitivity of the optimal nautical boundaries to the division rules and an indirect relationship between the allocated area and resource shares. The findings highlight the necessity for considering utility shares in negotiations as opposed to adopting areal division rules which ignore the utilities and might result in unfair resource allocation. The main policy implication of the study is that clarification of the countries' resource and areal gain under any suggested legal regime for governing the Caspian Sea is essential to the success of the negotiations.     

Distribution system feeder re-phasing considering voltage-dependency of loads

Phase-balancing creates voltage changes in the network which calls for incorporating voltage-dependency of loads in the process of phase-balancing. Hence, inclusion of voltage-dependency in current-injection based three-phase load flow is investigated and the results are compared with constant-power load model in terms of phase-balancing. The problem being combinatorial, application of particle swarm optimization is investigated for phase-balancing problem of radial distribution network. The effects of phase unbalance and load representation are studied in terms of various parameters. It is observed that there are situations that lead to increase in losses despite improvement in phase-balancing.     

基于遗传算法的格构增强复材夹芯板优化设计方法研究

格构增强复材夹芯板在土木工程领域已得到广泛应用,然而工程优化设计尚未得到解决。基于现有的格构增强复材夹芯板受力性能,提出了基于遗传算法的优化设计方法,并在此基础上对其建设成本进行优化计算。最后与已投入使用的格构增强泡沫复材夹芯板相比较,结果表明,在满足承载力的前提下,相比于原构件,优化后的构件建设成本显著降低,经济效益得以提升。     

Integrated multi-layer representation and ant colony search for product selective disassembly planning

Selective disassembly plays an important role in product life cycle to meet requirements of the product repairing, reusing and recycling. An efficient disassembly plan is essential to minimize processing time in product maintenance for cost saving. This paper introduces a method for integration of the multi-layer product representation and the optimal search in product selective disassembly planning. The multi-layer representation is based on the product structure formed in product design. The method enables an efficient search for the disassembly sequence. Unlike the existing product representation methods, the multi-layer representation is a dynamic product data model integrated with an ant colony search process for a near optimal solution. Industrial applications have proven the method effectiveness.     

A feature selection method based on modified binary coded ant colony optimization algorithm

Feature selection is a significant task for data mining and pattern recognition. It aims to select the optimal feature subset with the minimum redundancy and the maximum discriminating ability. In the paper, a feature selection approach based on a modified binary coded ant colony optimization algorithm (MBACO) combined with genetic algorithm (GA) is proposed. The method comprises two models, which are the visibility density model (VMBACO) and the pheromone density model (PMBACO). In VMBACO, the solution obtained by GA is used as visibility information; on the other hand, in PMBACO, the solution obtained by GA is used as initial pheromone information. In the method, each feature is treated as a binary bit and each bit has two orientations, one is for selecting the feature and another is for deselecting. The proposed method is also compared with that of GA, binary coded ant colony optimization (BACO), advanced BACO (ABACO), binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE) and a hybrid GA-ACO algorithm on some well-known UCI datasets; furthermore, it is also compared with some other existing techniques such as minimum Redundancy Maximum Relevance (mRMR), Relief algorithm for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.     

Optimal Design and Economic Evaluation of Dividing‐Wall Columns

In an effort to reduce costs, a systematic optimization approach is proposed to address the energy consumption of dividing‐wall columns (DWCs). This iterative optimization procedure begins by minimizing the overall heat duty using an innovative objective function within a constrained design space. A sensitivity analysis is then carried out on the manipulated variables to obtain their optimal ranges. Optimal operating parameters are obtained through the evaluation of the total annualized cost (TAC). For the separation process of benzene/toluene/o‐xylene, the optimal DWC flow sheet exhibits a significant decrease in TAC when compared to conventional flow sheet optimum designs. The applied optimization method and sensitivity analysis have proven to produce results at the global optimum. This method is both practical and easily applied to other systems, even to systems with more than three components.     

Hybrid optimization implemented for distributed generation parameters in a power system network

This paper presents a novel hybrid optimization algorithm for optimizing the Distributed Generation (DG) parameters in deregulated power system which improves the stability, reduces the losses and also increases the cost of generation. This Hybrid algorithm which includes Fuzzy-Genetic Algorithm (FGA) is used to optimize the various DG parameters simultaneously. The various parameters taken into consideration are their type, location and size of the DG devices. The simulation was performed on a distribution system and modeled for steady state studies. The optimization results are compared to the solution given by another search method like Genetic Algorithm (GA) and Micro Genetic Algorithm (MGA). The results reveal the benefits of the proposed method, for solving simultaneous combinorial problems of DG devices in a power system network.     

Applying portfolio theory-based modified ABC to electricity generation mix

Portfolio theory has found its model in numerous engineering applications for optimizing the electrical generation mix of an electricity area. However, to have better performance of this theory, this paper presents a new heuristic method as known modified artificial bee colony (MABC) to portfolio optimization problem. Moreover, we consider both dis-patchable and non-dis-patchable constrains variables and energy sources. Note that the proposed MABC method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm. Resulting, in this paper a portfolio theory-based MABC model that explicitly distinguishes between electricity generation (energy), installed capacity (power) and actual instantaneous power delivery is proposed. Therefore, in this model, the uncertainties of wind power and ramp-up/down constrains of traditional power plants are correctly considered in the investment cost. The numerical results show the great potential of proposed model with lowest risk on generation cost. Also, they are show that MABC approach is successful in portfolio optimization.     

Model predictive control with non-uniformly spaced optimization horizon for multi-timescale processes

Many chemical processes exhibit disparate timescale dynamics with strong coupling between fast, moderate and slow variables. To effectively handle this issue, a model predictive control (MPC) scheme with a non-uniformly spaced optimization horizon is proposed in this paper. This approach implements the time intervals that are small in the near future but large in the distant future, allowing the fast, moderate and slow dynamics to be included in the optimization whilst reducing the number of decision variables. A sufficient condition for ensuring stability for the proposed MPC is developed. The proposed approach is demonstrated using a case study of an industrial paste thickener control problem. While the performance of the proposed approach remains similar to a conventional MPC, it reduces the computational complexity significantly.