MotionEncoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles
Abstract
:1. Introduction
 
 The formulation of the optimization problem with a suitable objective function and the required constraints represents the targeted problem accurately.
 
 The use of motionencoding mechanism with ECPO to increase the efficacy of the algorithm. This duo has neither been tried before in the literature nor in solving any optimization problems.
 
 Comparing the proposed mechanism with 10 commonly used metaheuristic optimization algorithms strengthens the logic of using it in moving target search applications. It is also compared with MPSO, used in a recently published research paper to solve a similar optimization problem.
 
 The presentation of the convergence curves for all the used optimization methods in a single plot to ease the comparison of their performance.
2. Problem Formulation
2.1. Target Model
2.2. Sensor Model
2.3. Belief Map Update
2.4. Objective Function
3. MotionEncoded Electric Charged Particles Optimization (ECPOME) Algorithm
3.1. Description
 nECP: the total number of ECPs,
 MaxITER: the maximum number of iterations,
 nECPI: the number of ECPs which are interacting with themselves in one of the three strategies,
 naECP: the archive pool size.
3.2. Pseudocode
Algorithm 1 ECPO pseudocode 

3.3. Algorithm
3.3.1. Initialization
3.3.2. Archive Pool
3.3.3. Selection
3.3.4. Interaction
Strategy 1
Strategy 2
Strategy 3
3.3.5. Checking the Bounds
3.3.6. Diversification
3.3.7. Diversification
Algorithm 2 Pseudocode for the diversification phase 
1 For$\mathrm{i}=1:\mathrm{newECP}$ 2 For$\mathrm{j}=1:\text{}\mathrm{ProblemSize}$ 3 $\mathrm{I}\mathrm{f}\text{}\mathrm{rand}\mathrm{Pd}$ 4 select a random ECP from the archive pool (k) 5 $\mathrm{newECP}\left(\mathrm{i},\mathrm{j}\right)=\text{}\mathrm{archiveECP}\left(\mathrm{k},\mathrm{j}\right)$ 6 End If 7 End For 
3.3.8. Population Update
3.3.9. Criteria for Termination
3.3.10. Constraint Handling
3.3.11. Motion Encoding (ME)
4. Application, Results, and Discussion
4.1. Scenarios
4.2. Comparing Algorithms
4.3. Results
 
 For Scenario A, the proposed ECPOME algorithm obtained the best results for the ‘BEST’, the ‘MEAN’ and, the ‘MEDIAN values. The TLBO algorithm obtained the highest ‘WORST’ and ‘SD’ Values.
 
 For Scenario B, the TLBO obtained a slightly better value than the ECPOME in terms of the ‘BEST’ values. However, the ECPOME obtained the best results of the ‘MEAN’, the ‘MEDIAN, the ‘WORST’ and the ‘SD’ values compared to the remaining algorithms.
 
 For Scenario C and Scenario D, the proposed ECPOME outperformed the other algorithms in terms of the statistical performance indicators used in this study. It is worth mentioning that the DE achieved equally good results to ECPOME in terms of the ‘BEST’ values.
 
 For Scenario E, the proposed ECPOME showed better performance than all the remaining algorithms in terms of the ‘BEST’, the ‘MEAN’ and the ‘MEDIAN values while the DE achieved better results in terms of the ‘WORST’ and the ‘SD’ values.
 
 For Scenario F, the proposed ECPOME achieved a better result than all the remaining algorithms in terms of the ‘BEST’, the ‘MEDIAN’ and the ‘WORST’ values, while the TLBO was better in terms of the ‘MEAN’ and ‘SD’ values.
 
 All algorithms gave an FR equal to 100, which reflects that all of them could find a solution (i.e., a path) in all the runs and for all the investigated scenarios except for the ABC algorithm for Scenario D (FR = 93.33%).
 
 For scenarios with high probability regions, like Scenario C and Scenario D, the likelihood of finding the target is higher because there is no need to divide or spread the chances of finding the target in other areas.
4.4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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ECPOME  ECPO  MPSO  MVPA  DE  GA  ESDA  BBO  ABC  GSA  TLBO  BH  

Scenario A  BEST  0.20551  0.20116  0.18138  0.19810  0.20197  0.16030  0.18885  0.19948  0.18511  0.19572  0.20485  0.18101 
MEAN  0.19239  0.16555  0.06021  0.11085  0.18960  0.13368  0.14730  0.17851  0.14966  0.04258  0.19207  0.13570  
MEDIAN  0.19118  0.18091  0.03225  0.12225  0.18942  0.13315  0.15159  0.18132  0.16003  0.00095  0.19115  0.13742  
WORST  0.18209  0.03135  0.00000  0.00002  0.17250  0.10551  0.09942  0.11788  0.00131  0.00000  0.18303  0.06931  
SD  0.00593  0.04174  0.06305  0.06100  0.00630  0.01466  0.02609  0.01696  0.03707  0.06203  0.00587  0.02547  
FR  100  100  100  100  100  100  100  100  100  100  100  100  
Scenario B  BEST  0.27634  0.26252  0.22652  0.26014  0.27466  0.22486  0.23978  0.26156  0.24424  0.18925  0.27689  0.23919 
MEAN  0.25724  0.23126  0.10670  0.17272  0.24516  0.18563  0.18328  0.21522  0.19452  0.05717  0.25216  0.19299  
MEDIAN  0.25820  0.24153  0.11274  0.19613  0.24909  0.18932  0.18379  0.23196  0.20196  0.04139  0.25462  0.19735  
WORST  0.23530  0.14698  0.01105  0.03688  0.14928  0.15175  0.11802  0.11055  0.01471  0.00009  0.22876  0.13430  
SD  0.00964  0.02539  0.05659  0.06069  0.02339  0.01860  0.03786  0.04466  0.04892  0.05281  0.01293  0.02803  
FR  100  100  100  100  100  100  100  100  100  100  100  100  
Scenario C  BEST  0.68662  0.64070  0.58442  0.64361  0.68662  0.54835  0.64221  0.66811  0.61142  0.51114  0.67221  0.60402 
MEAN  0.64158  0.52614  0.30143  0.37015  0.62170  0.46797  0.47561  0.55109  0.49444  0.22860  0.63269  0.49182  
MEDIAN  0.64997  0.55979  0.31420  0.39419  0.63538  0.47210  0.48670  0.59358  0.52423  0.23693  0.63631  0.50913  
WORST  0.57162  0.26147  0.00240  0.01434  0.35432  0.36236  0.27549  0.23189  0.26933  0.00000  0.55967  0.36322  
SD  0.02876  0.10643  0.17289  0.15426  0.06193  0.05306  0.08845  0.10633  0.09406  0.13620  0.03111  0.07498  
FR  100  100  100  100  100  100  100  100  100  100  100  100  
Scenario D  BEST  0.55431  0.49309  0.47090  0.46095  0.55431  0.40220  0.51444  0.50274  0.46497  0.33458  0.53742  0.39930 
MEAN  0.48849  0.38766  0.24390  0.31673  0.45452  0.29675  0.31233  0.35144    0.20418  0.46575  0.28810  
MEDIAN  0.49887  0.38931  0.23998  0.32675  0.44347  0.29698  0.30451  0.33692    0.20486  0.46263  0.27994  
WORST  0.40148  0.26634  0.04982  0.09241  0.32458  0.21807  0.18826  0.22614    0.00002  0.37301  0.18647  
SD  0.03120  0.05620  0.08177  0.09298  0.06339  0.04411  0.07879  0.07564    0.08891  0.04044  0.05650  
FR  100  100  100  100  100  100  100  100  93.33  100  100  100  
Scenario E  BEST  0.20518  0.20358  0.18901  0.18726  0.20477  0.17868  0.18686  0.20130  0.19559  0.17188  0.20518  0.16870 
MEAN  0.19008  0.17367  0.11979  0.13986  0.18615  0.13753  0.14400  0.17809  0.16200  0.07077  0.18893  0.13598  
MEDIAN  0.18870  0.18154  0.12904  0.14625  0.18395  0.14029  0.14865  0.17725  0.16468  0.07634  0.18830  0.13544  
WORST  0.17187  0.11000  0.00021  0.02299  0.17277  0.09305  0.04521  0.15401  0.04478  0.00012  0.16969  0.07844  
SD  0.00801  0.02362  0.05092  0.03505  0.00787  0.01839  0.03434  0.01107  0.02810  0.05553  0.00867  0.02228  
FR  100  100  100  100  100  100  100  100  100  100  100  100  
Scenario F  BEST  0.22728  0.21985  0.16005  0.17661  0.22195  0.16416  0.20217  0.22195  0.20572  0.17721  0.22175  0.18615 
MEAN  0.20007  0.18020  0.07334  0.10851  0.19650  0.10638  0.13038  0.19142  0.15022  0.06385  0.20008  0.13070  
MEDIAN  0.21024  0.18573  0.07296  0.13480  0.20160  0.11310  0.14440  0.20465  0.15892  0.03687  0.20930  0.13941  
WORST  0.16650  0.08188  0.00129  0.00564  0.13640  0.04128  0.03171  0.08111  0.00041  0.00070  0.16648  0.02830  
SD  0.01978  0.03226  0.05025  0.05427  0.02234  0.03378  0.04813  0.03334  0.05207  0.06128  0.01820  0.04135  
FR  100  100  100  100  100  100  100  100  100  100  100  100 
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Alanezi, M.A.; Bouchekara, H.R.E.H.; Shahriar, M.S.; Sha’aban, Y.A.; Javaid, M.S.; Khodja, M. MotionEncoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles. Sensors 2021, 21, 6568. https://doi.org/10.3390/s21196568
Alanezi MA, Bouchekara HREH, Shahriar MS, Sha’aban YA, Javaid MS, Khodja M. MotionEncoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles. Sensors. 2021; 21(19):6568. https://doi.org/10.3390/s21196568
Chicago/Turabian StyleAlanezi, Mohammed A., Houssem R. E. H. Bouchekara, Mohammad S. Shahriar, Yusuf A. Sha’aban, Muhammad S. Javaid, and Mohammed Khodja. 2021. "MotionEncoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles" Sensors 21, no. 19: 6568. https://doi.org/10.3390/s21196568