scholarly journals A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 598
Author(s):  
Lin Wang ◽  
Ronghua Shi ◽  
Jian Dong
Keyword(s):  
Vertical Displacement ◽  
Continuous Functions ◽  
Convergence Speed ◽  
Knapsack Problems ◽  
Stability And Convergence ◽  
Algorithm Optimization ◽  
Solution Quality ◽  
Dragonfly Algorithm ◽  
Algorithm Stability ◽  
Angle Modulation

The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms.

Optimal design of differential mount using nature-inspired optimization methods

2021 ◽  
Vol 63 (8) ◽  
pp. 764-769
Author(s):  
Emre İsa Albak ◽  
Erol Solmaz ◽  
Ferruh Öztürk
Keyword(s):  
Optimum Design ◽  
Weight Reduction ◽  
Lightweight Design ◽  
Reduction Process ◽  
Optimization Methods ◽  
Convergence Speed ◽  
Structural Performance ◽  
Grey Wolf Optimizer ◽  
Solution Quality ◽  
Dragonfly Algorithm

Abstract Structural performance and lightweight design are a significant challenge in the automotive industry. Optimization methods are essential tools to overcome this challenge. Recently, nature-inspired optimization methods have been widely used to find optimum design variables for the weight reduction process. The objective of this study is to investigate the best differential mount design using nature-based optimum design techniques for weight reduction. The performances of the nature-based algorithms are tested using convergence speed, solution quality, and robustness to find the best design outlines. In order to examine the structural performance of the differential mount, static analyses are performed using the finite element method. In the first step of the optimization study, a sampling space is generated by the Latin hypercube sampling method. Then the radial basis function metamodeling technique is used to create the surrogate models. Finally, differential mount optimization is performed by using genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), ant lion optimizer (ALO) and dragonfly algorithm (DA), and the results are compared. All methods except PSO gave good and close results. Considering solution quality, robustness and convergence speed data, the best optimization methods were found to be MFO and ALO. As a result of the optimization, the differential mount weight is reduced by 14.6 wt.-% compared to the initial design.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

scholarly journals An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Yiquan Du ◽  
Xiuguo Zhang ◽  
Zhiying Cao ◽  
Shaobo Wang ◽  
Jiacheng Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Short Term Memory ◽  
Convergence Speed ◽  
Planning Method ◽  
Learning Ability ◽  
Stability And Convergence ◽  
State Information ◽  
Reward Function ◽  
Discrete Action ◽  
The Impact

Deep Reinforcement Learning (DRL) is widely used in path planning with its powerful neural network fitting ability and learning ability. However, existing DRL-based methods use discrete action space and do not consider the impact of historical state information, resulting in the algorithm not being able to learn the optimal strategy to plan the path, and the planned path has arcs or too many corners, which does not meet the actual sailing requirements of the ship. In this paper, an optimized path planning method for coastal ships based on improved Deep Deterministic Policy Gradient (DDPG) and Douglas–Peucker (DP) algorithm is proposed. Firstly, Long Short-Term Memory (LSTM) is used to improve the network structure of DDPG, which uses the historical state information to approximate the current environmental state information, so that the predicted action is more accurate. On the other hand, the traditional reward function of DDPG may lead to low learning efficiency and convergence speed of the model. Hence, this paper improves the reward principle of traditional DDPG through the mainline reward function and auxiliary reward function, which not only helps to plan a better path for ship but also improves the convergence speed of the model. Secondly, aiming at the problem that too many turning points exist in the above-planned path which may increase the navigation risk, an improved DP algorithm is proposed to further optimize the planned path to make the final path more safe and economical. Finally, simulation experiments are carried out to verify the proposed method from the aspects of plan planning effect and convergence trend. Results show that the proposed method can plan safe and economic navigation paths and has good stability and convergence.

Improved Chaotic Bacteria Foraging Optimization Algorithm Particle

2014 ◽  
Vol 651-653 ◽  
pp. 2322-2325
Author(s):  
Ying Ai ◽  
Yi Xin Su ◽  
Dan Hong Zhang ◽  
Yao Peng
Keyword(s):  
Optimization Algorithm ◽  
Chaotic Map ◽  
Standard Test ◽  
Convergence Speed ◽  
Test Functions ◽  
Algorithm Optimization ◽  
Slow Convergence ◽  
And Migration ◽  
Logistic Chaotic Map ◽  
Bacteria Foraging Optimization Algorithm

. Aiming at the defects of weak global search ability and slow convergence speed in bacteria foraging algorithm optimization, this paper proposed an improved chaotic bacteria foraging optimization algorithm which has introduced the chaotic thoughts, improved the update operation of fitness and migration operation in optimization process. Using Logistic chaotic map initializes the bacteria population, so as to improve the convergence speed of the algorithm; Then adjust quorum sensing mechanism to optimize the chemotactic direction of the bacteria, and operate on perished bacteria with chaos disturbance in migration operation, so as to improve the global optimization ability of the algorithm. Simulation of two standard test functions show that the proposed algorithm has higher convergence speed and precision.

scholarly journals A High-Performance Stochastic Fractal Search Algorithm for Optimal Generation Dispatch Problem

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1796 ◽  
Author(s):  
Ly Huu Pham ◽  
Minh Quan Duong ◽  
Van-Duc Phan ◽  
Thang Trung Nguyen ◽  
Hoang-Nam Nguyen
Keyword(s):  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Stability And Convergence ◽  
Major Influence ◽  
Search Performance ◽  
Solution Quality ◽  
Update Process ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

This paper proposes applications of a modified stochastic fractal search algorithm (MSFS) to solve the economic load dispatch problem (ELD) in which valve-point effects, prohibited operating zones, power losses in all conductors, multi-fuel sources and other constraints of power system are taken into consideration. The proposed method is first developed in the study by performing two modifications on two procedures of new solution generation from conventional stochastic fractal search (SFS). The first modification is used to change the strategy of producing new solutions of the first and the second update procedures while the second one is to newly update the worst solutions in the first update process and the best solutions in the second update process. These modifications have major influence on the solution search performance of the proposed method. All improvements of the proposed method can be illustrated by solving and analyzing results from various test systems with different system scales including 3-unit, 6-unit, 10-unit, and 20-unit systems. Comparison of results obtained by MSFS, SFS, and other existing methods indicates that the proposed MSFS method is more effective and robust than compared methods in terms of solution quality, high-quality solution search stability and convergence process. Consequently, the proposed method should be used as a very favorable optimization method for the ELD problem and it should be tried for other optimization problems in electrical engineering.

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