Disturbed Exploitation compact Differential Evolution for limited memory optimization problems

Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

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Differential Evolution with Population and Strategy Parameter Adaptation

Mathematical Problems in Engineering ◽

10.1155/2015/287607 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 11

Author(s):

V. Gonuguntla ◽

R. Mallipeddi ◽

Kalyana C. Veluvolu

Keyword(s):

Differential Evolution ◽

Population Size ◽

Optimization Problems ◽

Search Space ◽

Benchmark Problems ◽

Parameter Adaptation ◽

De Algorithm ◽

Probabilistic Technique ◽

Number Of Individuals ◽

Strategy Parameters

Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.

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An Improved Differential Evolution for Constrained Multi-Objective Optimization Problems

2020 16th International Conference on Computational Intelligence and Security (CIS) ◽

10.1109/cis52066.2020.00064 ◽

2020 ◽

Author(s):

Erping Song ◽

Hecheng Li ◽

Cuo Wanma

Keyword(s):

Differential Evolution ◽

Optimization Problems ◽

Multi Objective Optimization ◽

Multi Objective

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Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

Periodica Polytechnica Civil Engineering ◽

10.3311/ppci.12544 ◽

2018 ◽

Author(s):

Ali Kaveh ◽

S.R. Hoseini Vaez ◽

Pedram Hosseini

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Structural Optimization ◽

Differential Evolution ◽

Optimization Problems ◽

Harmony Search ◽

Average Weight ◽

Weight Optimization ◽

Swarm Optimization ◽

Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

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