scholarly journals BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY

2015 ◽  
pp. 107-112
Author(s):  
Sunanda Gupta ◽  
Sakshi Arora
Keyword(s):  
Simulated Annealing ◽  
Probability Function ◽  
Fitness Landscape ◽  
Optimal Solution ◽  
Search Space ◽  
Solution Quality ◽  
Acceptance Probability ◽  
Evaluation Strategies ◽  
Fitness Distance Correlation ◽  
The Cost

Multi Dimensional Knapsack problem is a widely studied NP hard problem requiring extensive processing to achieve optimality. Simulated Annealing (SA) unlike other is capable of providing fast solutions but at the cost of solution quality. This paper focuses on making SA robust in terms of solution quality while assuring faster convergence by incorporating effective fitness landscape parameters. For this it proposes to modify the ‘Acceptance Probability’ function of SA. The fitness landscape evaluation strategies are embedded to Acceptance Probability Function to identify the exploitation and exploration of the search space and analyze the behavior on the performance of SA. The basis of doing so is that SA in the process of reaching optimality ignores the association between the search space and fitness space and focuses only on the comparison of current solution with optimal solution on the basis of temperature settings at that point. The idea is implemented in two different ways i.e. by making use of Fitness Distance Correlation and Auto Correlation functions. The experiments are conducted to evaluate the resulting SA on the range of MKP instances available in the OR library.

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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Xufeng Yuan ◽  
Dongyuan Shi ◽  
Yu He ◽  
...  
Keyword(s):  
Power Grid ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Search Space ◽  
Neighborhood Search ◽  
Solution Quality ◽  
Promising Alternative ◽  
Comparison Results ◽  
Population Based Algorithm

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.

scholarly journals Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem

2018 ◽  
Vol 12 (11) ◽  
pp. 366 ◽  
Author(s):  
Issam AlHadid ◽  
Khalid Kaabneh ◽  
Hassan Tarawneh
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Search Space ◽  
Local Optimum ◽  
Solution Quality ◽  
University Course Timetabling ◽  
Hybrid Simulated Annealing ◽  
Complex Optimization ◽  
List Memory ◽  
University Course

Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.

Empirical Investigation of the Benefits of Partial Lamarckianism

1997 ◽  
Vol 5 (1) ◽  
pp. 31-60 ◽  
Author(s):  
Christopher R. Houck ◽  
Jeffery A. Joines ◽  
Michael G. Kay ◽  
James R. Wilson
Keyword(s):  
Empirical Investigation ◽  
Fitness Landscape ◽  
Nonlinear Problems ◽  
Search Space ◽  
Test Problems ◽  
Solution Quality ◽  
Minimax Criterion ◽  
Worst Case ◽  
Local Optima ◽  
Improvement Procedures

Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of improvement procedures, which are good at finding local optima, and GAs. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the location-allocation problem, and the cell formation problem were used as test problems in an empirical investigation. Neither a pure Lamarckian nor a pure Baldwinian search strategy was found to consistently lead to quicker convergence of the GA to the best known solution for the series of test problems. Based on a minimax criterion (i.e., minimizing the worst case performance across all test problem instances), the 20% and 40% partial Lamarckianism search strategies yielded the best mixture of solution quality and computational efficiency.

scholarly journals An Effective Heuristic-Based Approach for Partitioning

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Embedded System ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Local Optimal Solution ◽  
Annealing Algorithm ◽  
The Cost ◽  
Combined Algorithm ◽  
Search Capability

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

scholarly journals An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Bili Chen ◽  
Wenhua Zeng ◽  
Yangbin Lin ◽  
Qi Zhong
Keyword(s):  
Simulated Annealing ◽  
Multiobjective Optimization ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Acceptance Probability ◽  
Nondominated Solutions ◽  
Multiobjective Optimization Problems ◽  
Annealing Algorithm

An enhanced differential evolution based algorithm, named multi-objective differential evolution with simulated annealing algorithm (MODESA), is presented for solving multiobjective optimization problems (MOPs). The proposed algorithm utilizes the advantage of simulated annealing for guiding the algorithm to explore more regions of the search space for a better convergence to the true Pareto-optimal front. In the proposed simulated annealing approach, a new acceptance probability computation function based on domination is proposed and some potential solutions are assigned a life cycle to have a priority to be selected entering the next generation. Moreover, it incorporates an efficient diversity maintenance approach, which is used to prune the obtained nondominated solutions for a good distributed Pareto front. The feasibility of the proposed algorithm is investigated on a set of five biobjective and two triobjective optimization problems and the results are compared with three other algorithms. The experimental results illustrate the effectiveness of the proposed algorithm.

scholarly journals Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem

2018 ◽  
Vol 12 (11) ◽  
pp. 385
Author(s):  
Issam AlHadid ◽  
Khalid Kaabneh ◽  
Hassan Tarawneh
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Search Space ◽  
Local Optimum ◽  
Solution Quality ◽  
University Course Timetabling ◽  
Hybrid Simulated Annealing ◽  
Complex Optimization ◽  
List Memory ◽  
University Course

Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.

scholarly journals Metode Simulated Annealing untuk Optimasi Penjadwalan Perkuliahan Perguruan Tinggi

2016 ◽  
Vol 6 (2) ◽  
pp. 133
Author(s):  
Wiktasari Sari ◽  
Jatmiko Endro Suseno
Keyword(s):  
Simulated Annealing ◽  
Time Slot ◽  
Heuristic Method ◽  
Optimal Solution ◽  
Search Space ◽  
Course Scheduling ◽  
Time Period ◽  
Method Of Solution ◽  
Class Room ◽  
Annealing Method

Course scheduling an assignment of courses and lecturers in the available time slots involving certain restrictions. Simulated annealing is a heuristic method can be used as search method and provide acceptable solutions with good results. The research aims to make scheduling courses at the college using simulated annealing using five variables data that lecturer courses, the time slot is comprised of the day and the time period and class room. The research has two objective functions to be generated, the first is the assignment of a lecturer on courses that will be of teaching, second lecturers and their assignment course on the time slot and the room available. The objective function is calculated by taking into account the restrictions involved to produce the optimal solution. The validation is performed by testing to simulated annealing method with an varian average of 77.791% of the data variance can reach a solution with a standard deviation of 3.931509. In this research given the method of solution in the use of the remaining search space to be reused by the data that is unallocated.

scholarly journals Aluminium Parts Casting Scheduling Based on Simulated Annealing

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 741
Author(s):  
Antonio Jiménez-Martín ◽  
Alfonso Mateos ◽  
Josefa Z. Hernández
Keyword(s):  
Simulated Annealing ◽  
Electricity Consumption ◽  
Optimal Solution ◽  
Casting Process ◽  
Production Costs ◽  
Injection Process ◽  
Aluminium Production ◽  
Unmet Demand ◽  
Gas Consumption ◽  
The Cost

This paper focuses on the last stage of the aluminium production process in the context of Industry 4.0: schedule optimization in the casting process. Casting is one of the oldest manufacturing processes in which a liquid material is usually poured into a mold that contains a hollow cavity of the desired shape and then allowed to solidify. This is a complex scheduling problem in which several constraints, such as different maintenance processes, maximum stocks, machine breakdowns, work shifts, or the maximum number of mold changes per day, come into play. Four objective functions have to be taken into account simultaneously. We have to minimize both the unmet demand at the end of the schedule, and the delays in the injection process with regard to daily demands. Production costs, including the cost of electricity consumption in the injection process and gas consumption associated with melting furnaces, should be minimized. Finally, the total number of mold changes throughout the schedule must also be reduced to a minimum. The simulated annealing (SA) metaheuristic has been adapted to solve this complex optimization process and parameterized for application to a wide variety of aluminium making processes. SA efficiently solves the problem and provides an optimal solution in about three minutes.

scholarly journals Cost-Effective Placement of Recharging Stations in Drone Path Planning for Surveillance Missions on Large Farms

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1661
Author(s):  
Jean Louis Ebongue Kedieng Fendji ◽  
Israel Kolaigue Bayaola ◽  
Christopher Thron ◽  
Marie Danielle Fendji ◽  
Anna Förster
Keyword(s):  
Simulated Annealing ◽  
Path Planning ◽  
Optimal Solution ◽  
Cost Effective ◽  
Planning Problem ◽  
Area Of Interest ◽  
Energy Limitation ◽  
Large Farm ◽  
Feasible Solutions ◽  
The Cost

The energy limitation remains one of the biggest constraints in drone path planning, since it prevents drones from performing long surveillance missions. To assist drones in such missions, recharging stations have recently been introduced. They are platforms where the drone can autonomously land to recharge its battery before continuing the mission. However, the cost of those platforms remains a significant obstacle to their adoption. Consequently, it is important to reduce their number while planning the path of the drone. This work introduces the Single Drone Multiple Recharging Stations on Large Farm problem (SD-MRS-LF). A large farm is considered as an area of interest to cover with a set of candidate locations where recharging stations can be installed. The aim is to determine the path of the drone that minimizes the number of locations for recharging stations as well as the completion time of the surveillance mission. This path planning problem falls within the realm of computational geometry and is related to similar problems that are encountered in the field of robotics. The problem is complicated due to environmental constraints on farms such as wind speed and direction, which produce asymmetries in the optimal solution. A back-and-forth-k-opt simulated annealing (BFKSA) approach is proposed to solve the defined problem. The new approach is compared to the basic back-and-forth (BF) and a K-opt variant of the well-known simulated annealing (KSA) approach over a set of 20 random topologies in different environmental conditions. The results from computational experiments show that the BFKSA approach outperforms the others, in terms of providing feasible solutions and minimizing the number of recharges.

scholarly journals Research on Social Talent Governance Based on Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yan Zeng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Convergence Rate ◽  
Single Point ◽  
Optimal Solution ◽  
Implementation Process ◽  
Recall Rate ◽  
Acceptance Probability ◽  
High Data ◽  
Calculation Results

The existing social talent governance algorithms have a number of issues such as slow convergence rate, relatively low data accuracy, recall rate, and low anti-interference. To address these problems, this paper proposes a research on social talent governance algorithm based on genetic algorithm. We discuss the difference between the traditional and the genetic algorithms and determine the implementation process of genetic algorithm. On this basis, the excellent individuals are determined by independent computing fitness, and the initialization population is designed according to the individual similarity threshold. After the population is defined, the roulette and deterministic sampling selection method are integrated to clarify the selection calculation process. Based on the calculation results, we design the crossover operator by segmented single-point crossover between individuals. The mutation operator is designed by segmented mutation of different gene segments according to the calculation results. The results are incorporated into the simulated annealing acceptance probability to conduct simulated annealing for the individuals after the cross-mutation operation and set relevant conditions after the end of the algorithm. We seek the optimal solution of the data within the number of iterations and finally realize the whole process of social talent governance algorithm. The experimental results show that the proposed algorithm has fast convergence rate, high data precision and recall, and has certain feasibility.

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