Quality Of Final Solution Research Articles

Differential Evolution algorithm for contingency analysis-based optimal location of FACTS controllers in deregulated electricity market

In this paper, a novel heuristic optimization algorithm called differential evolution (DE) technique has been proposed to solve the optimal location of FACTS devices in deregulated electricity market using contingency analysis. The proposed approach deals with the optimal injection of real and reactive power using FACTS devices with an aim to minimize the total system cost, the total number of overloads, excess power flow, the severity of overload and real power loss of the network. To enhance the search behavior of the DE approach, the scaling factor and crossover parameters are empirically selected. To show the superiority of the proposed DE approach and as a step ahead to prove its novelty, the same optimization problem is solved using evolutionary programming method, and the results are compared. The results indicate the proposed DE algorithm is superior in terms of final solution quality, efficiency, convergence rate and robustness. A sample four-bus system and 24-bus EHV southern region of Indian grid system has been considered to illustrate the proposed methodology.

Enhanced differential evolution with modified parent selection technique for numerical optimisation

Differential evolution (DE) is considered to be one of the most prominent evolutionary algorithms for numerical optimisation. However, it may suffer from the slow convergence rate, especially in the late stage of evolution progress. The reason might be that the parents in the mutation operator are randomly selected from the parent population. To remedy this limitation and to enhance the performance of DE, in this paper, a modified parent selection technique is proposed, where the parents in the mutation operator are chosen based on their previous successful experiences. The major advantages of the proposed parent selection technique are its simplicity and generality. It does not destroy the simple structure of DE, and it can be used in most DE variants. To verify the performance of the proposed technique, it is integrated into the classical DE algorithm and three advanced DE variants. Thirteen widely used benchmark functions are used as the test suite. Experimental results indicate that the proposed technique is able to enhance the performance of the classical DE and advanced DE algorithms in terms of both the quality of final solutions and the convergence rate.

A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve‐point effects. In order to effectively solve the problem, a simple yet very young and efficient population‐based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across‐search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems.

Gravitational search algorithm and K-means for simultaneous feature selection and data clustering: a multi-objective approach

Clustering is an unsupervised classification method used to group the objects of an unlabeled data set. The high dimensional data sets generally comprise of irrelevant and redundant features also along with the relevant features which deteriorate the clustering result. Therefore, feature selection is necessary to select a subset of relevant features as it improves discrimination ability of the original set of features which helps in improving the clustering result. Though many metaheuristics have been suggested to select subset of the relevant features in wrapper framework based on some criteria, most of them are marred by the three key issues. First, they require objects class information a priori which is unknown in unsupervised feature selection. Second, feature subset selection is devised on a single validity measure; hence, it produces a single best solution biased toward the cardinality of the feature subset. Third, they find difficulty in avoiding local optima owing to lack of balancing in exploration and exploitation in the feature search space. To deal with the first issue, we use unsupervised feature selection method where no class information is required. To address the second issue, we follow pareto-based approach to obtain diverse trade-off solutions by optimizing conceptually contradicting validity measures silhouette index (Sil) and feature cardinality (d). For the third issue, we introduce genetic crossover operator to improve diversity in a recent Newtonian law of gravity-based metaheuristic binary gravitational search algorithm (BGSA) in multi-objective optimization scenario; it is named as improved multi-objective BGSA for feature selection (IMBGSAFS). We use ten real-world data sets for comparison of the IMBGSAFS results with three multi-objective methods MBGSA, MOPSO, and NSGA-II in wrapper framework and the Pearson’s linear correlation coefficient (FM-CC) as a multi-objective filter method. We employ four multi-objective quality measures convergence, diversity, coverage and ONVG. The obtained results show superiority of the IMBGSAFS over its competitors. An external clustering validity index F-measure also establish the above finding. As the decision maker picks only a single solution from the set of trade-off solutions, we employee the F-measure to select a final single solution from the external archive. The quality of final solution achieved by IMBGSAFS is superior over competitors in terms of clustering accuracy and/or smaller subset size.

Lagrangian Heuristics for Large-Scale Dynamic Facility Location with Generalized Modular Capacities

We consider the dynamic facility location problem with generalized modular capacities, a multiperiod facility location problem in which the costs for capacity changes may differ for all pairs of capacity levels. The problem embeds a complex cost structure and generalizes several existing facility location problems, such as those that allow temporary facility closing or capacity expansion and reduction. As the model may become very large, general-purpose mixed-integer programming (MIP) solvers are limited to solving instances of small to medium size. In this paper, we extend the generalized model to the case of multiple commodities. We propose Lagrangian heuristics, based on subgradient and bundle methods, to find good quality solutions for large-scale instances with up to 250 facility locations and 1,000 customers. To improve the final solution quality, a restricted MIP model is solved based on the information collected through the solution of the Lagrangian dual. Computational results show that the Lagrangian-based heuristics provide highly reliable results for all problem variants considered. They produce good quality solutions in short computing times even for instances where state-of-the-art MIP solvers do not find feasible solutions. The strength of the formulation also allows the method to provide tight bounds on the optimal value. Data and the online appendix are available at https://doi.org/10.1287/ijoc.2016.0738 .

An integrated approach for maintenance planning by considering human factors: Application to a petrochemical plant

Abstract In this paper a novel approach is presented for maintenance planning by considering grouping strategy and human factors. The proposed approach describes various steps from system configuration to maintenance plan review. In previous studies, it has been shown that grouping maintenance activities would reduce total maintenance cost by saving set-up costs. In order to model a more realistic situation, work complexity is incorporated to the model. Also, a special attention is paid to human factors during maintenance planning to investigate whether human error would be increased by performing various activities at the same period. It is shown fatigue and time pressure have impact on the preferred maintenance plan. Consecutive maintenance grouping approach is compared with two well-known meta-heuristic algorithms including genetic algorithm and simulated annealing. In order to show the applicability of the proposed approach a petrochemical plant in Iran is selected as a case study. According to the results of the case study, however from theoretical point of view, consecutive maintenance grouping is not capable of handling variable setup cost and work complexity, it could be used for generating initial solution for meta-heuristic algorithms to improve the quality of final solution.

Solving stochastic programming problems using new approach to Differential Evolution algorithm

This paper presents a new approach to Differential Evolution algorithm for solving stochastic programming problems, named DESP. The proposed algorithm introduces a new triangular mutation rule based on the convex combination vector of the triangle and the difference vector between the best and the worst individuals among the three randomly selected vectors. The proposed novel approach to mutation operator is shown to enhance the global and local search capabilities and to increase the convergence speed of the new algorithm compared with conventional DE. DESP uses Deb’s constraint handling technique based on feasibility and the sum of constraint violations without any additional parameters. Besides, a new dynamic tolerance technique to handle equality constraints is also adopted. Two models of stochastic programming (SP) problems are considered: Linear Stochastic Fractional Programming Problems and Multi-objective Stochastic Linear Programming Problems. The comparison results between the DESP and basic DE, basic particle swarm optimization (PSO), Genetic Algorithm (GA) and the available results from where it is indicated that the proposed DESP algorithm is competitive with, and in some cases superior to, other algorithms in terms of final solution quality, efficiency and robustness of the considered problems in comparison with the quoted results in the literature.

Grey wolf optimizer for unmanned combat aerial vehicle path planning

Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem, which aims to obtain an optimal or near-optimal flight route with the threats and constraints in the combat field well considered. A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV two-dimension path planning problem. Then, the UCAV can find the safe path by connecting the chosen nodes of the two-dimensional coordinates while avoiding the threats areas and costing minimum fuel. Conducted simulations show that the proposed method is more competent for the UCAV path planning scheme than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of final solutions.

Constrained global optimization via a DIRECT-type constraint-handling technique and an adaptive metamodeling strategy

Recent metamodel-based global optimization algorithms are very promising for box-constrained expensive optimization problems. However, few of them can tackle constrained optimization problems. This article presents an improved constrained optimization algorithm, called eDIRECT-C, for expensive constrained optimization problems. In the eDIRECT-C algorithm, we present a novel DIRECT-type constraint-handling technique that separately handles feasible and infeasible cells. This technique has no user-defined parameter and is beneficial for exploring the undetected feasible regions and boundary of feasible regions. We also employ an adaptive metamodeling strategy to build appropriate metamodel types for objective and constraints respectively. This strategy yields more accurate predictions and therefore significantly speeds up the convergence. To assess the performance of eDIRECT-C, we compare it with some state-of-the-art metamodel-based constrained optimization algorithms and the original DIRECT algorithm on 13 benchmark problems and 4 engineering examples. The comparative results imply that the proposed algorithm is very promising for constrained problems in terms of the convergence speed, quality of final solutions and success rate.

A novel meta-heuristic algorithm: Dynamic Virtual Bats Algorithm

Nature-inspired algorithms are a very important part of meta-heuristics. A novel nature inspired algorithm called the Dynamic Virtual Bats Algorithm (DVBA) is presented in this paper. DVBA is inspired by a bat’s ability to manipulate frequency and wavelength of the emitted sound waves when hunting. A role based search has been developed to improve the diversification and intensification capability of Bat Algorithm. In the DVBA, there are only two bats: explorer and exploiter bat. While the explorer bat explores the search space, the exploiter bat makes an intensive search of the local with the highest probability of locating the desired target. Depending on their location, bats exchange the roles dynamically.The performance of the DVBA is extensively evaluated on a suite of 30 bound-constrained optimization problems from CEC 2014 and compared favorably with other well-known meta-heuristics algorithms. The experimental results demonstrated that the proposed DVBA outperform, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates.

The Impact of Short Term Traffic Forecasting on the Effectiveness of Vehicles Routes Planning in Urban Areas

An impossibility to foresee in advance the accurate traffic parameters in face of dynamism phenomena in complex transportation system is a one of the major source of uncertainty. The paper presents an approach to robust optimization of logistics vehicle routes in urban areas on the basis of estimated short-term traffic time forecasts in a selected area of the urban road network. The forecast values of optimization parameters have been determined using the spectral analysis model, taking into account the forecast uncertainty degree. The robust counterparts approach of uncertain bi-criteria shortest path problem formulation is used to determining the robust routes for logistics vehicles in the urban network. The uncertainty set is created on the basis of forecast travel times in chosen sections, estimated by means of spectral analysis. The advantages and the characteristics are exemplified in the actual Krakow road network. The obtained data have been compared with classic approach wherein it is assumed that the optimization parameters are certain and accurate. The results obtained in the simulation example indicate that use of forecasting techniques with robust optimization models has a positive impact on the quality of final solutions.

A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm.

This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.

Computational Chemotaxis in Micro Bacterial Foraging Optimization for High Dimensional Problems: A Comparative Study on Numerical Benchmark

Nature and bio-inspired algorithms have been recently used for solving high dimensional search and optimization problems. In this context, bacterial foraging optimization algorithm (BFOA) has been widely employed as a global optimization technique inspired from social foraging behavior of Escheria coli bacteria. In this paper, a novel hybrid technique called micro Chemotaxis Differential Evolution Optimization Algorithm (CDEOA) that uses a small population is proposed. In this technique, we incorporate the principles of DE (Differential Evolution) into BFOA. The best bacterium retains its position whereas the rest of the population are reinitialized on the search space. CDEOA was compared with classical BFOA with two different population sizes and micro BFOA (BFOA) over a suite of 16 numerical optimization problems taken from P.N. Suganthan. Statistics of the computer simulations indicate that CDEOA outperforms, or is comparable to, its competitors in terms of its convergence rates and quality of final solution for complex high dimensional problems.

PBCOPSO: A Parallel Optimization Algorithm for Task Scheduling in Cloud Environment

Objectives: Cloud environment requires scheduling of independent tasks with the available resources to minimize the total execution time and to optimize the resource utilization in cloud environment. Methods: Evolutionary algorithms are widely used to find the suboptimal solution of a problem. This work adopts a parallel approach that considers Bee Colony Optimization (BCO) in parallel with Particle Swarm Optimization (PSO) for cloud task scheduling. The proposed approach is named as Parallel Bee Colony Optimization Particle Swarm Optimization (PBCOPSO). Findings: The results show that the proposed approach minimizes Makespan with optimized resource utilization. It is observed that the proposed method improved resource utilization by an average of 5.0383% when compared with Min-Min algorithm and by an average of 3.7243% when compared with Improved Bee Colony Optimization (IBCO). Novelty of the Study: The proposed hybrid PBCOPSO enables improved search in the solution space due to the parallel execution of BCO and PSO leading to better final solution quality and lower execution time. Conclusion: Thus two metrics namely makespan and resource utilization are evaluated and an optimal task to resource mapping is achieved with hybridization.

A Hybrid Algorithm Based on Bat-Inspired Algorithm and Differential Evolution for Constrained Optimization Problems

How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solving the COPs. Traditional BAs are prone to sink into stagnation or local optima when no bat individual founds a better location than the past locations for several generations. DE is adopted for updating the past location of bat individuals to force BA to jump out of stagnation or local optima, since it has a great local searching capability. The performance of BA-DE algorithm is improved by the proposed hybrid mechanism. We use 24 well-known benchmark functions to verify the overall performance of our proposed algorithm. Comparisons show that BA-DE outperforms most advanced methods in terms of the final solution's quality.

Hybrid achievement oriented computational chemotaxis in bacterial foraging optimization: a comparative study on numerical benchmark

The social foraging behavior of Escherichia coli bacteria has been recently used for solving complex real-world search and optimization problems. Bacterial foraging optimization algorithm (BFOA) is an important global optimization method inspired from this behavior. In this paper, a novel method called chemotaxis differential evolution optimization algorithm (CDEOA), which augments BFOA with conditional introduction of differential evolution (DE) and Random Search operators, is proposed. Introduction of these operators is done considering the number of successful run and unsuccessful tumble steps of bacteria. CDEOA was compared with the classical BFOA, two variants of BFOA which use DE operators [Adaptive Chemotactic Bacterial Swarm Foraging Optimization with Differential Evolution Strategy (ACBSFO_DES)], chemotaxis differential evolution (CDE), and the classical DE on all 30 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014) Special Session and Competition on Single Objective Real Parameter Numerical Optimization suite. CDEOA was also compared with four state-of-the-art DE variants that competed in CEC 2014. Statistics of the computer simulations over this benchmark suite indicate that CDEOA outperforms, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates for high-dimensional problems.

Parameter extraction of different fuel cell models with transferred adaptive differential evolution

To improve the design and control of FC (fuel cell) models, it is important to extract their unknown parameters. Generally, the parameter extraction problems of FC models can be transformed as nonlinear and multi-variable optimization problems. To extract the parameters of different FC models exactly and fast, in this paper, we propose a transferred adaptive DE (differential evolution) framework, in which the successful parameters of the adaptive DE solving previous problems are properly transferred to solve new optimization problems in the similar problem-domains. Based on this framework, an improved adaptive DE method (TRADE, in short) is presented as an illustration. To verify the performance of our proposal, TRADE is used to extract the unknown parameters of two types of fuel cell models, i.e., PEMFC (proton exchange membrane fuel cell) and SOFC (solid oxide fuel cell). The results of TRADE are also compared with those of other state-of-the-art EAs (evolutionary algorithms). Even though the modification is very simple, the results indicate that TRADE can extract the parameters of both PEMFC and SOFC models exactly and fast. Moreover, the V–I characteristics obtained by TRADE agree well with the simulated and experimental data in all cases for both types of fuel cell models. Also, it improves the performance of the original adaptive DE significantly in terms of both the quality of final solutions and the convergence speed in all cases. Additionally, TRADE is able to provide better results compared with other EAs.

Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.

Artificial Bee Colony Algorithm with Adaptive Explorations and Exploitations: A Novel Approach for Continuous Optimization

A proper balance between global explorations and local exploitations is often considered necessary for complex, high dimensional optimization problems to avoid local optima and to find a good near optimum solution with sufficient convergence speed. This paper introduces Artificial Bee Colony algorithm with Adaptive eXplorations and eXploitations (ABC-AX), a novel algorithm that improves over the basic Artificial Bee Colony (ABC) algorithm. ABC-AX augments each candidate solution with three control parameters that control the perturbation rate, magnitude of perturbations and proportion of explorative and exploitative perturbations. Together, all the control parameters try to adapt the degree of global explorations and local exploitations around each candidate solution by affecting how new trial solutions are produced from the existing ones. The control parameters are automatically adapted at the individual solution level, separately for each candidate solution. ABC-AX is tested on a number of benchmark problems of continuous optimization and compared with the basic ABC algorithm and several other recent variants of ABC algorithm. Results show that the performance of ABC-AX is often better than most other algorithms in comparison, in terms of both convergence speed and final solution quality.

A simulated annealing algorithm for university course timetabling considering travelling distances

This paper addresses the curriculum-based course timetabling problem CB-CCT with extra travelling distance constraints using an improved simulated annealing algorithm. The motivation of considering the new constraints is that the travelling distances between two consecutive lectures attended by the same group of students must be controlled into a certain range in order to facilitate on-campus transportation. We propose a two-stage solution method, which circumvents the hard constraints violations firstly, and minimises the soft constraints violations subsequently. To obtain satisfactory solutions, an improved simulated annealing algorithm with memory function ISAM is adopted, which incorporates a variety of space adjustment and time adjustment strategies as the local search module. We study the impact of the initial solution, the annealing strategy, and the search depth of ISAM on the final solution quality. The computational results show that the improved simulated annealing algorithm can solve the course timetabling problem effectively.