Global replacement-based differential evolution with neighbor-based memory for dynamic optimization

Zhu, Zhen; Chen, Long; Yuan, Chaochun; Xia, Changgao

doi:10.1007/s10489-018-1147-9

Cited by 21 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Brownian and quantum individuals help in controlling the population diversity and thereby, enhancing the search efficiency. Zhu et al (2018) proposed a replacement-based DE algorithm, which is based on the "DE/best/1" mutation operator. The novel replacement operator helps the population move towards the optima gradually.…”

Section: (A) Multi-populationsmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

View full text Add to dashboard Cite

Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

show abstract

Section: (A) Multi-populationsmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

View full text Add to dashboard Cite

show abstract

“…[2], [7]- [27] Moving peaks baselines b [3], [15], [18], [23], [25]- [173] Composition of basic static functions c [6], [75], [97], [106], [111], [114], [115], [146]- [148], [150]- [172], [174] Others d [24], [175]- [177] a Basic static functions include Sphere, Ackley, Rastrigin, Rosenbrock, and Griewank. b Including all baseline functions that generate a controllable number of peaks whose locations can change over time.…”

Section: Baseline Function Referencesmentioning

confidence: 99%

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

Yazdani

Cheng

Yazdani

et al. 2021

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…The set of test functions in this benchmark is described in Equation (7), where a(t) is the environment at time step t, h i (t), w i (t), c i (t) is the height, width and center of the i-th peak function at time t, respectively;…”

Section: Moving Peaks Benchmark 2 (Mpb2)mentioning

confidence: 99%

“…Evolutionary algorithms, such as Differential Evolution (DE), have shown good performance to solve tracking problems [5][6][7]. However, the search and implementation of the optimum each time the environment changes may not be feasible due to different circumstances, such as time or cost.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution in Robust Optimization Over Time Using a Survival Time Approach

Guzmán-Gaspar

Mezura-Montes

Domínguez-Isidro

2020

MCA

View full text Add to dashboard Cite

This study presents an empirical comparison of the standard differential evolution (DE) against three random sampling methods to solve robust optimization over time problems with a survival time approach to analyze its viability and performance capacity of solving problems in dynamic environments. A set of instances with four different dynamics, generated by two different configurations of two well-known benchmarks, are solved. This work also introduces a comparison criterion that allows the algorithm to discriminate among solutions with similar survival times to benefit the selection process. The results show that the standard DE holds a good performance to find ROOT solutions, improving the results reported by state-of-the-art approaches in the studied environments. Finally, it was found that the chaotic dynamic, disregarding the type of peak movement in the search space, is a source of difficulty for the proposed DE algorithm.

show abstract

Global replacement-based differential evolution with neighbor-based memory for dynamic optimization

Cited by 21 publications

References 39 publications

A survey on evolutionary computation for complex continuous optimization

A survey on evolutionary computation for complex continuous optimization

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades—Part B

Differential Evolution in Robust Optimization Over Time Using a Survival Time Approach

Contact Info

Product

Resources

About