Opposition-based Differential Evolution with protective generation jumping

Esmailzadeh, Ali; Rahnamayan, Shahryar

doi:10.1109/sde.2011.5952059

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…To automatically tune the jumping rate of OBL, Rahnamayan et al proposed an ODE variant called ODE with a time-varying jumping rate (ODETVJR) and found that a linearly decreasing jumping rate is more effective than a linearly increasing one [61]. To prevent the waste of fitness evaluations, Esmailzadeh and Rahnamayan proposed an ODE variant called ODE with protective generation jumping (ODEPGJ), which stops OBL when the success rate of the opposite solutions decreases monotonously from a predefined threshold [62]. In [63], a quasi-ODE (QODE) was proposed, using quasi opposite solutions that were mathematically proven to be more likely to be located near the optimal solution of a given problem than opposite ones.…”

Section: Related Workmentioning

confidence: 99%

“…We conducted experiments to evaluate the performance of iBetaCODE and compared it to ten state-of-the-art ODE variants, namely: 1) ODE [13], 2) ODETVJR [61], 3) ODEPGJ [62], 4) QODE [63], 5) QRODE [64], 6) COODE [65], 7) GODE [66], 8) EODE [67], 9) AGODE [68], and 10) Beta-CODE [14]. For a fair comparison, we used the same classical DE variant DE/rand/1/bin; regarding the control parameters associated with the DE, we used the following values: F = 0.5, CR = 0.9, and NP = 100.…”

Section: Comparison With Ten Ode Variantsmentioning

confidence: 99%

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A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

Choi¹,

Togelius²,

Cheong³

2019

Preprint

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A new variant of stochastic opposition-based learning (OBL) is proposed in this paper. OBL is a relatively new machine learning concept, which consists of simultaneously calculating an original solution and its opposite to accelerate the convergence of soft computing algorithms. Recently a new opposition-based differential evolution (ODE) variant called BetaCODE was proposed as a combination of differential evolution and a new stochastic OBL variant called BetaCOBL. BetaCOBL is capable of flexibly adjusting the probability density functions used to calculate opposite solutions, generating more diverse opposite solutions, and preventing the waste of fitness evaluations. While it has shown outstanding performance compared to several state-of-the-art OBL variants, BetaCOBL is challenging with more complex problems because of its high computational cost. Besides, as it assumes that the decision variables are independent, there is a limitation in the search for decent opposite solutions on inseparable problems. In this paper, we propose an improved stochastic OBL variant that mitigates all the limitations of BetaCOBL. The proposed algorithm called iBetaCOBL reduces the computational cost from O(NP 2 •D) to O(NP•D) (NP and D stand for population size and dimension, respectively) using a linear time diversity measure. In addition, iBetaCOBL preserves the strongly dependent decision variables that are adjacent to each other using the multiple exponential crossover. The results of the performance evaluations on a set of 58 test functions show that iBetaCODE finds more accurate solutions than ten state-of-the-art ODE variants including BetaCODE. Additionally, we applied iBetaCOBL to two state-of-the-art DE variants, and as in the previous results, iBetaCOBL based variants exhibit significantly improved performance.

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Section: Related Workmentioning

confidence: 99%

Section: Comparison With Ten Ode Variantsmentioning

confidence: 99%

A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

Choi¹,

Togelius²,

Cheong³

2019

Preprint

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“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ continuous optimization functions, and the results revealed that the OBL strategy contributed to the metaheuristics' convergence. This work inspired the formulation of new OBL approaches, as well as their applications in different metaheuristics, such as Differential Evolution (DE) [6], [7], [8], [9], Particle Swarm Optimization(PSO) [10], [11], [12], [13], [14], Harmony Search (HS) [15], [16], [17],Simulated Annealing (SA) [18],Salp Swarm algorithms (SSA) [19], [20], [21] and SCA [2], [22].…”

Section: Introductionmentioning

confidence: 99%

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

Bhattacharya

Nayak

et al. 2023

IEEE Access

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Meta-heuristics are commonly applied to solve various global optimization problems. In order to make the meta-heuristics performing a global search, balancing their exploration and exploration ability is still an open avenue. This manuscript proposes a novel Opposition-based learning scheme, called ''PCOBL'' (Partial Centroid Opposition-based Learning), inspired by the partial centroid. PCOBL aims to improve meta-heuristics performance through maintaining an effective balance between the exploration and exploitation. PCOBL was incorporated in three different meta-heuristics, and a comparative study was conducted on 28 CEC2013 benchmark problems with 30, 50, and 100 dimensions. In addition, we assessed the PCOBL in the IEEE CEC2011 real-world problems. The empirical results demonstrate that PCOBL balances the exploration and exploitation ability of the meta-heuristics, positively impacting their performance and making them outperform the state-of-the-art algorithms in terms of best-error runs and convergence in most of the optimization problems. Moreover, the computational cost analysis illustrated that the inclusion of PCOBL in the meta-heuristic algorithm has a low impact on its efficiency.

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“…Hence, many variants of the OBL strategy have been reported to strengthen MAs in terms of trade-offs between exploration and exploitation. The authors in [ 26 ] propose an opposition-based DE having a protective jumping rate, which achieves stopping the opposite operator when the success rate of the opposite individual falls to a constant threshold. In [ 27 ], OBL with the current optimum DE algorithm is proposed, and its concept is that instead of using the center point to calculate the opposite point, the best point of the current point is utilized.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Zhang

et al. 2022

Entropy

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Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.

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Opposition-based Differential Evolution with protective generation jumping

Cited by 14 publications

References 12 publications

A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

PCOBL: A Novel Opposition-Based Learning Strategy to Improve Metaheuristics Exploration and Exploitation for Solving Global Optimization Problems

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

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