scholarly journals A novel approach for solving travelling thief problem using enhanced simulated annealing

2021 ◽  
Vol 7 ◽  
pp. e377
Author(s):  
Hamid Ali ◽  
Muhammad Zaid Rafique ◽  
Muhammad Shahzad Sarfraz ◽  
Muhammad Sheraz Arshad Malik ◽  
Mohammed A. Alqahtani ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Minimum Cost ◽  
Medium Size ◽  
Time Interval ◽  
Maximum Profit ◽  
Novel Approach ◽  
Hard Problems

Real-world optimization problems are getting more and more complex due to the involvement of inter dependencies. These complex problems need more advanced optimizing techniques. The Traveling Thief Problem (TTP) is an optimization problem that combines two well-known NP-Hard problems including the 0/1 knapsack problem and traveling salesman problem. TTP contains a person known as a thief who plans a tour to collect multiple items to fill his knapsack to gain maximum profit while incurring minimum cost in a standard time interval of 600 s. This paper proposed an efficient technique to solve the TTP problem by rearranging the steps of the knapsack. Initially, the picking strategy starts randomly and then a traversal plan is generated through the Lin-Kernighan heuristic. This traversal is then improved by eliminating the insignificant cities which contribute towards profit adversely by applying the modified simulated annealing technique. The proposed technique on different instances shows promising results as compared to other state-of-the-art algorithms. This technique has outperformed on a small and medium-size instance and competitive results have been obtained in the context of relatively larger instances.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

scholarly journals Parameterized Nonlinear Least Squares for Unsupervised Nonlinear Spectral Unmixing

2019 ◽  
Vol 11 (2) ◽  
pp. 148 ◽  
Author(s):  
Risheng Huang ◽  
Xiaorun Li ◽  
Haiqiang Lu ◽  
Jing Li ◽  
Liaoying Zhao
Keyword(s):  
Least Squares ◽  
Optimization Problem ◽  
Nonnegative Matrix Factorization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Synthetic Data ◽  
Nonnegative Matrix ◽  
Nonlinear Least Squares ◽  
Hyperspectral Data ◽  
Spectral Unmixing

This paper presents a new parameterized nonlinear least squares (PNLS) algorithm for unsupervised nonlinear spectral unmixing (UNSU). The PNLS-based algorithms transform the original optimization problem with respect to the endmembers, abundances, and nonlinearity coefficients estimation into separate alternate parameterized nonlinear least squares problems. Owing to the Sigmoid parameterization, the PNLS-based algorithms are able to thoroughly relax the additional nonnegative constraint and the nonnegative constraint in the original optimization problems, which facilitates finding a solution to the optimization problems . Subsequently, we propose to solve the PNLS problems based on the Gauss–Newton method. Compared to the existing nonnegative matrix factorization (NMF)-based algorithms for UNSU, the well-designed PNLS-based algorithms have faster convergence speed and better unmixing accuracy. To verify the performance of the proposed algorithms, the PNLS-based algorithms and other state-of-the-art algorithms are applied to synthetic data generated by the Fan model and the generalized bilinear model (GBM), as well as real hyperspectral data. The results demonstrate the superiority of the PNLS-based algorithms.

Matched Field Processing Based on the Simulated Annealing

2013 ◽  
Vol 651 ◽  
pp. 879-884
Author(s):  
Qi Wang ◽  
Ying Min Wang ◽  
Yan Ni Gou
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Mediterranean Sea ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Combinatorial Optimization Problem ◽  
Matched Field Processing ◽  
Combinatorial Optimization Problems ◽  
Passive Localization

The matched field processing (MFP) for localization usually needs to match all the replica fields in the observation sea with the received fields, and then find the maximum peaks in the matched results, so how to find the maximum in the results effectively and quickly is a problem. As known the classical simulated annealing (CSA) which has the global optimization capability is used widely for combinatorial optimization problems. For passive localization the position of the source can be recognized as a combinatorial optimization problem about range and depth, so a new matched field processing based on CSA is proposed. In order to evaluate the performance of this method, the normal mode was used to calculate the replica field. Finally the algorithm was evaluated by the dataset in the Mediterranean Sea in 1994. Comparing to the conventional matched field passive localization (CMFP), it can be conclude that the new one can localize optimum peak successfully where the output power of CMFP is maximum, meanwhile it is faster than CMFP.

scholarly journals AntNet: Distributed Stigmergetic Control for Communications Networks

1998 ◽  
Vol 9 ◽  
pp. 317-365 ◽  
Author(s):  
G. Di Caro ◽  
M. Dorigo
Keyword(s):  
Adaptive Learning ◽  
Mobile Agents ◽  
Optimization Problems ◽  
State Of The Art ◽  
Routing Algorithms ◽  
Superior Performance ◽  
Experimental Conditions ◽  
Communications Networks ◽  
Novel Approach ◽  
Routing Tables

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

scholarly journals Profile inference revisited

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-24
Author(s):  
Wenlei He ◽  
Julián Mestre ◽  
Sergey Pupyrev ◽  
Lei Wang ◽  
Hongtao Yu
Keyword(s):  
Optimization Problem ◽  
Theoretical Foundation ◽  
State Of The Art ◽  
Minimum Cost ◽  
Control Flow ◽  
Extended Model ◽  
Extensive Evaluation ◽  
Inference Techniques ◽  
Maximum Flows ◽  
Profile Accuracy

Profile-guided optimization (PGO) is an important component in modern compilers. By allowing the compiler to leverage the program’s dynamic behavior, it can often generate substantially faster binaries. Sampling-based profiling is the state-of-the-art technique for collecting execution profiles in data-center environments. However, the lowered profile accuracy caused by sampling fully optimized binary often hurts the benefits of PGO; thus, an important problem is to overcome the inaccuracy in a profile after it is collected. In this paper we tackle the problem, which is also known as profile inference and profile rectification . We investigate the classical approach for profile inference, based on computing minimum-cost maximum flows in a control-flow graph, and develop an extended model capturing the desired properties of real-world profiles. Next we provide a solid theoretical foundation of the corresponding optimization problem by studying its algorithmic aspects. We then describe a new efficient algorithm for the problem along with its implementation in an open-source compiler. An extensive evaluation of the algorithm and existing profile inference techniques on a variety of applications, including Facebook production workloads and SPEC CPU benchmarks, indicates that the new method outperforms its competitors by significantly improving the accuracy of profile data and the performance of generated binaries.

Optimal Tolerance Allocation Over Multiple Manufacturing Alternatives

1992 ◽  
Author(s):  
Jonathan Cagan ◽  
Thomas R. Kurfess
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Minimum Cost ◽  
Tolerance Allocation ◽  
Hyperbolic Function ◽  
Manufacturing Processes ◽  
Worst Case ◽  
Annealing Algorithm ◽  
Manufacturing Alternatives

Abstract We introduce a methodology for concurrent design that considers the allocation of tolerances and manufacturing processes for minimum cost. Cost is approximated as a hyperbolic function over tolerance, and worst-case stack-up tolerance is assumed. Two simulated annealing techniques are introduced to solve the optimization problem. The first assumes independent, unordered, manufacturing processes and uses a Monte-Carlo simulation; the second assumes well known individual process cost functions which can be manipulated to create a single continuous function of cost versus tolerance with discontinuous derivatives solved with a continuous simulated annealing algorithm. An example utilizing a system of friction wheels over the manufacturing processes of turning, grinding, and saw cutting bar stock demonstrates excellent results.

Improved Hybrid Taboo Search with its Application in Computer-Aided Optimization Problem

2012 ◽  
Vol 488-489 ◽  
pp. 1293-1297
Author(s):  
Jia Yang Wang ◽  
Bi Zhang ◽  
Zuo Yong Li ◽  
Lei Xu
Keyword(s):  
Simulated Annealing ◽  
Computer Aided Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Test Functions ◽  
Local Optima ◽  
Taboo Search ◽  
Computer Aided ◽  
Aided Design ◽  
Improved Algorithm

A new improved algorithm of Taboo Search (TS), namely, Hybrid Taboo Search (HTS) is first introduced and tried for several test functions having multiple local optima. Here, Taboo Search was improved by combining Immune Arithmetic (IA) and Simulated Annealing (SA). Several strategies to improve the TS have been presented before, but the focus here is on the novelty, availability and precision of algorithm. There are several optimization problems in computer-aided design, so the article used the improved HTS in computer-aided optimization problems, the performance of which is compared with the performance of conventional TS (TS). Results show that HTS plays an important role in solving computer-aided optimization problems with the effectiveness and higher accuracy.

A Dimensional Diversity Based Hybrid Multiobjective Evolutionary Algorithm for Optimization Problem

2016 ◽  
Vol 30 (07) ◽  
pp. 1659020 ◽  
Author(s):  
Peng Wang ◽  
Changsheng Zhang ◽  
Bin Zhang ◽  
Tingting Liu ◽  
Jiaxuan Wu
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Results ◽  
Selection Scheme ◽  
Genetic Operator ◽  
Hybrid Evolutionary Algorithm ◽  
Multiobjective Optimization Problems

Multiobjective density driven evolutionary algorithm (MODdEA) has been quite successful in solving multiobjective optimization problems (MOPs). To further improve its performance and address its deficiencies, this paper proposes a hybrid evolutionary algorithm based on dimensional diversity (DD) and firework explosion (FE). DD is defined to reflect the diversity degree of population dimension. Based on DD, a selection scheme is designed to balance diversity and convergence. A hybrid variation based on FE and genetic operator is designed to facilitate diversity of population. The proposed algorithm is tested on 14 tests problems with diverse characteristics and compared with three state-of-the-art designs. Experimental results show that the proposed design is better or at par with the chosen state-of-the-art algorithms for multiobjective optimization.

Does Complex Metaheuristic Out-Perform Simple Hill-Climbing for Optimization Problems? A Simulation Evaluation

2013 ◽  
Vol 748 ◽  
pp. 666-669 ◽  
Author(s):  
Xing Wen Zhang
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Noise ◽  
Computation Time ◽  
Hill Climbing ◽  
Supply Chain Optimization ◽  
Solution Quality ◽  
Metaheuristic Methods

In this paper we compare the performance of metaheuristic methods, namely simulated annealing and Tabu Search, against simple hill climbing heuristic on a supply chain optimization problem. The benchmark problem we consider is the retailer replenishment optimization problem for a retailer selling multiple products. Computation and simulation results demonstrate that simulated annealing and Tabu search improve solution quality. However, the performance improvement is less in simulations with random noise. Lastly, simulated annealing appears to be more robust than Tabu search, and the results justify its extra implementation effort and computation time when compared against hill climbing.

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