scholarly journals Dynamic immune cooperative scheduling of agricultural machineries

2021 ◽  
Author(s):  
Xiaoyan Liu ◽  
Xinmeng Zhu ◽  
Kuangrong Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Performance Indicator ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Pareto Solutions ◽  
Nsga Ii ◽  
Mutation Strategy ◽  
Static Partitioning ◽  
Cooperative Scheduling

AbstractConsidering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

Application of Tabu-Genetic Algorithm in Reactive Power Optimization

2014 ◽  
Vol 1006-1007 ◽  
pp. 1021-1025
Author(s):  
Song Tao Zhang ◽  
Gong Bao Wang ◽  
Hui Bo Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Reactive Power Optimization ◽  
Simple Genetic Algorithm ◽  
Better Than

By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

Diagnostic Strategy Optimization Based on Predatory Search Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 2441-2444
Author(s):  
Jin Luo ◽  
Qi Bin Deng ◽  
Chen Meng
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Global Search ◽  
State Probability ◽  
Diagnostic Strategy ◽  
Local Optimal Solution ◽  
Predatory Search Algorithm

With respect to the inherent NP-hard complexity of Optimization of testability diagnostic strategy problem, a predatory search algorithm simulating animal predatory strategies was designed. This algorithm adopted the gross test expense including state probability, isolation matrix and test expense as its objective function, defined local and global search by the restriction value of search space based on two points exchange, and realized the conversion between local and global search by adjusting the restriction value of search space. It had better ability to conduct local search and jump out of local optimal solution simultaneously, and provided a better resolution for the optimization of testability diagnostic strategy.

scholarly journals Application of Improved Sparrow Search Algorithm in Concrete

2021 ◽  
Vol 2082 (1) ◽  
pp. 012014
Author(s):  
Chengtian Ouyang ◽  
Feng Tang ◽  
Donglin Zhu ◽  
Yaxian Qiu ◽  
Yujia Liu
Keyword(s):  
Local Search ◽  
High Stability ◽  
Search Algorithm ◽  
Concrete Strength ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Strength Prediction

Abstract Compared with other algorithms, the performance of sparrow algorithm is better, but it also has shortcomings such as insufficient convergence and large randomness. For this reason, this paper proposes an improved sparrow search algorithm, which uses K-means to initialize the population to reduce the influence of randomness. Use sine and cosine search to improve the quality of the position of followers, and finally use adaptive local search to update the optimal solution, and apply it to concrete strength prediction. The results show that various improved sparrow search algorithms have certain advantages and high stability.

scholarly journals Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Lan-Fen Liu ◽  
Xin-Feng Yang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Search Algorithm ◽  
Production Costs ◽  
Total Production ◽  
Local Search Algorithm ◽  
Aggregate Production Planning ◽  
Nsga Ii ◽  
Aggregate Production ◽  
Multi Objective

AbstractThe diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

A New Hybrid Binary-Real Coded Cuckoo Search and Tabu Search Algorithm for Solving the Unit-Commitment Problem

2021 ◽  
Vol 10 (2) ◽  
pp. 104-119
Author(s):  
Amel Terki ◽  
Hamid Boubertakh
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Unit Commitment Problem ◽  
Local Optima

This paper proposes a new intelligent optimization approach to deal with the unit commitment (UC) problem by finding the optimal on/off states strategy of the units under the system constraints. The proposed method is a hybridization of the cuckoo search (CS) and the tabu search (TS) optimization techniques. The former is distinguished by its efficient global exploration mechanism, namely the levy flights, and the latter is a successful local search method. For this sake, a binary code is used for the status of units in the scheduled time horizon, and a real code is used to determine the generated power by the committed units. The proposed hybrid CS and TS (CS-TS) algorithm is used to solve the UC problem such that the CS guarantees the exploration of the whole search space, while the TS algorithm deals with the local search in order to avoid the premature convergence and prevent from trapping into local optima. The proposed method is applied to the IEEE standard systems of different scales ranging from 10 to 100 units. The results show clearly that the proposed method gives better quality solutions than the existing methods.

An Improved Tabu Search Algorithm for Global Optimization of Engineering Design Problems

2013 ◽  
Vol 441 ◽  
pp. 762-767
Author(s):  
Ning Wang ◽  
Shi You Yang
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Tabu Search Algorithm ◽  
Global Optimal Solution ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Multimodal Function ◽  
Global Optimal ◽  
Continuous And Discrete Variables

To find the global optimal solution of a multimodal function with both continuous and discrete variables, an improved tabu search algorithm is proposed. The improvements include new generating mechanisms for initial and neighborhood solutions, the exclusive use of the tabu list, the restarting methodology for different cycle of iterations as well as the shifting away from the worst solutions. The numerical results on two numerical examples are reported to demonstrate the feasibility and merit of the proposed algorithm.

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