Improved Backtracking Search Algorithm Research Articles

Dynamic reconfiguration of active distribution network based on improved backtracking search algorithm

This research proposes a dynamic reconfiguration model (DRM) and method for the distribution network, considering wind power, photovoltaic distributed generation (DG), and demand-side response. The reconfiguration goal is to minimize the total operating cost of the distribution network. The electricity purchase costs, DG operation costs, participation in demand response programs, network losses, and voltage deviations are selected to construct the optimization function. The DRM is established by clustered load data segments. An improved backtracking search algorithm incorporating a differential evolution learning strategy and adaptive chaotic elite search strategy is adopted to solve the DRM. The viability of the proposed method is validated by an IEEE 30-node simulation distributed system.

Optimal allocation of photovoltaic generations in buildings‐to‐distribution‐network integration system using improved backtracking search optimization algorithm

AbstractA novel optimal allocation framework for photovoltaic generations in an integration system of buildings‐to‐distribution‐network using improved backtracking search optimization algorithm is proposed here. In the proposed framework, photovoltaic generations are optimally allocated to optimize the overall performance of a buildings‐to‐distribution‐network regarding the efficient active power usage of photovoltaic generations, the energy savings, and voltage profile improvement of distribution network. The effects of building active demand response on the photovoltaic generations' optimal allocation are considered in the proposed framework. An improved backtracking search algorithm using two new operators is developed to optimize the active power reduction factors and locations of photovoltaic generations. The test results of IEEE 33‐bus and 69‐bus systems demonstrate that the developed framework can take full advantage of photovoltaic generation power and the active demand response of buildings to coordinate the efficient active power usage of photovoltaic generations, the voltage profile improvement and energy savings of a buildings‐to‐distribution‐network. In addition, the improved backtracking search optimization algorithm converges faster than genetic algorithm, classical backtracking search optimization algorithm, and bird swarm algorithm.

A Bio-Inspired Multi-Population-Based Adaptive Backtracking Search Algorithm.

Backtracking search algorithm (BSA) is a nature-based optimization technique extensively used to solve various real-world global optimization problems for the past few years. The present work aims to introduce an improved BSA (ImBSA) based on a multi-population approach and modified control parameter settings to apprehend an ensemble of various mutation strategies. In the proposed ImBSA, a new mutation strategy is suggested to enhance the algorithm’s performance. Also, for all mutation strategies, the control parameters are updated adaptively during the algorithm’s execution. Extensive experiments have been performed on CEC2014 and CEC2017 single-objective benchmark functions, and the results are compared with several state-of-the-art algorithms, improved BSA variants, efficient differential evolution (DE) variants, particle swarm optimization (PSO) variants, and some other hybrid variants. The nonparametric Friedman rank test has been conducted to examine the efficiency of the proposed algorithm statistically. Moreover, six real-world engineering design problems have been solved to examine the problem-solving ability of ImBSA. The experimental results, statistical analysis, convergence graphs, complexity analysis, and the results of real-world applications confirm the superior performance of the suggested ImBSA.

An improved Backtracking search algorithm for the flexible job shop rescheduling problem with new job insertions

An improved Backtracking search algorithm for the flexible job shop rescheduling problem with new job insertions

An improved backtracking search algorithm for the flexible job shop rescheduling problem with new job insertions

An improved backtracking search algorithm for the flexible job shop rescheduling problem with new job insertions

An effective backtracking search algorithm for multi-objective flexible job shop scheduling considering new job arrivals and energy consumption

This work addresses the flexible job shop scheduling problem considering new job arrivals which is a common occurrence in real-world manufacturing enterprises. With growing concerns regarding the increasing energy consumption and the advent of green manufacturing, it is essential to consider energy-related objectives in scheduling. Hence, we formulate a mathematical model for the flexible job shop scheduling problem considering new job arrivals and turn on/off strategy with an objective to minimize the makespan, energy consumption, and instability. We address this problem by using an improved backtracking search algorithm. We propose an effective crossover operator, to improve the algorithm’s searchability and prevent premature convergence. A slack-based insertion rescheduling strategy is developed to handle new job insertions in the schedule. Taguchi analysis is employed to identify the best combination of algorithm parameters. Finally, we generate new benchmark instances for the flexible job shop scheduling problem with new job arrivals. We validate the superior performance of the proposed insertion strategy and the algorithm in terms of solution quality through comprehensive experiments.

Optimal distribution network configuration using improved backtracking search algorithm

Optimal network configuration is one of the effective approaches for power loss reduction of the distribution network. This paper shows a network reconfiguration method using improved backtracking search algorithm (IBSA). Wherein, IBSA is improved in the process of generating randomly the initialization population. The network reconfiguration method based on IBSA is used to find the optimal network configuration for the 33-node and 69-node systems. The results are compared to the original backtracking search algorithm (BSA), particle swarm optimization (PSO), firefly algorithm (FA) and previous approaches. From the compared results, IBSA can determine the optimal network configuration with higher success rate than BSA, PSO, FA and lower power loss than other previous approaches. As a result, IBSA is an effective approach for finding the optimal network configuration.

Optimal Strategy of Active Distribution Network Considering Source–Network–Load

With the access of renewable distributed generations and the uncertainty of demand-side response, the operation states of the active distribution network (ADN) are uncertain. This study proposes a novel optimal strategy taking active management costs of the source–network–load as the goal and considering the site selection and the incentive price for the responsive load to optimise the ADN. To deal with uncertainties, a chance-constrained optimisation model is established and the Monte Carlo sampling method is used. Then, the uncertain model is settled by the improved backtracking search algorithm, which uses an adaptive mutation scale factor. Finally, the practicability of the proposed strategy and the performance of the algorithm are verified by the modified IEEE 33-bus distribution system and an actual distribution system in China.

Parameter Identification of Pump Turbine Governing System Using an Improved Backtracking Search Algorithm

Accurate parameter identification of pump turbine governing system (PTGS) is of great importance to the precise modeling of pumped storage unit. As PTGS is characterized by uncertainties and strong nonlinear characteristics, it is difficult to identify its parameters. To solve the parameter identification problem for PTGS, an improved backtracking search algorithm (IBSA) is proposed by combining the original BSA with the orthogonal initialization technique, the chaotic local search operator, the elastic boundary processing strategy and the adaptive mutation scale factor. The proposed IBSA algorithm for parameter identification of PTGS was applied on an illustrative example to demonstrate its accuracy and efficiency. The simulation results have shown that IBSA performed better compared with the particle swarm optimization, the gravitational search algorithm and the original BSA in regard to solution quality and parameter identification accuracy.

Improved backtracking search algorithm for renewable energy system

This paper presents an improved version of the Backtracking Search Algorithm (BSA).BSA have the advantage to memorize the previous best fitness value to lead the population to the global minimum. Although BSA has a strong robustness to the best fitness value, it shows a poor ability in convergence. The algorithm called Improved Backtracking Search algorithm (IBSA) is proposed to optimize the total cost of the energy produced from renewable resources. An experimental study has been conducted and the result shows the performance of the proposed algorithm.

An improved backtracking search algorithm for casting heat treatment charge plan problem

This study investigates the optimization of the charge plan in casting heat treatment. The optimization problem is formulated as a 0–1 integer programming model aiming at maximizing the utilization of the furnaces, minimizing the holding temperature differences and the overall delivery deadline of castings in a furnace. To approach the mathematical model, a two-steps solution methodology is designed. First, the feasible casting candidate sets are generated in consideration of the holding temperature and cooling mode constraints. Then, an improved backtracking search algorithm (IBSA) is proposed to obtain optimal charge plan for each feasible candidate set. The best one among the optimal charge plans obtained by IBSA is selected as the final charge plan. In IBSA, a mapping mechanism is applied to make original backtracking search algorithm (BSA) suitable to discrete problems. Improvements that consist of the modification of historical population updating mechanism, the hybrid of mutation and crossover strategy of difference evaluation algorithm, a greedy local search algorithm and the re-initialization operator are also made to enhance the exploitation and exploration ability of IBSA. The comparisons of simulation experiments demonstrate the effectiveness of the proposed model and the performance of the proposed algorithm.

An exact penalty function method for optimising QAP formulation in facility layout problem

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system’s operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.

Parameter Identification of Nonlinear Muskingum Model with Backtracking Search Algorithm

Nonlinear Muskingum model is a popular approach widely used for flood routing in hydraulic engineering. An improved backtracking search algorithm (BSA) is proposed to estimate the parameters of nonlinear Muskingum model. The orthogonal designed initialization population strategy and chaotic sequences are introduced to improve the exploration and exploitation ability of BSA. At the same time, a selection strategy based individual feasibility violation is developed to ensure that the computed outflows are non-negative in the evolutionary process. Finally, three examples are employed to demonstrate the performance of the improved BSA. The comparison between the results of routing outflows and those of Wilcoxon signed ranks test shows that the improved BSA outperforms particle swarm optimization, genetic algorithm, differential evolution and other algorithms reported in the literature in terms of solution quality. Therefore, it is reasonable to draw the conclusion that the proposed BSA is a satisfactory and efficient choice for parameter estimation of nonlinear Muskingum model.