Opposition-Based Differential Evolution

Rahnamayan, Shahryar; Tizhoosh, Hamid R.; Salama, M.M.A.

doi:10.1007/978-3-540-68830-3_6

Cited by 245 publications

(359 citation statements)

References 47 publications

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“…These values have been widely employed in the literature (Vesterstrøm and Thomsen, 2004;Ali and Törn, 2004;Rahnamayan et al, 2008;Sacco et al, 2014). For BBBC, we employed NP = 100 (population size).…”

Section: Implementation and Set-upmentioning

confidence: 99%

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

Sacco

Rios-Coelho

2016

IJNEST

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Abstract:In this work, we introduce a new Metropolis algorithm, which is an enhancement of the recent Particle Collision Algorithm (PCA), loosely inspired by the neutron interactions in a reactor. This novel method is called the CrossSection Particle Collision Algorithm (CSPCA), as it incorporates the concept of cross-section from Reactor Physics, in the sense that points in the search space and their respective fitness-function values are analogous to the neutron cross-sections which are used to express the likelihood of interaction between an incident neutron and a target nucleus. CSPCA is compared against the original PCA and two state-of-the-art metaheuristics, differential evolution and big bang-big crunch. These methods are applied to the turbine balancing problem, which is an NP-hard (i.e. non-deterministic polynomial-time hard) combinatorial optimisation problem that can be used to assess the potential of an algorithm to be applied to Fuel Management Optimisation (FMO). CSPCA performs better than its opponents, showing potential to be used not only in FMO, but also in other nuclear science and engineering optimisation problems.

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Section: Implementation and Set-upmentioning

confidence: 99%

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

Sacco

Rios-Coelho

2016

IJNEST

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“…All experiments were run 60 times independently. Mean, s.d., highest value and lowest values of the final fitness obtained from 60 runs by the proposed ABCDE are compared with those by original ABC as well as opposition-based DE (ODE) [24]. ODE is a state-of-the-art DE enhanced with the opposition-based learning in population initialization, generation jumping, and local improvement.…”

Section: A Experimentation Setupmentioning

confidence: 99%

“…ODE is a state-of-the-art DE enhanced with the opposition-based learning in population initialization, generation jumping, and local improvement. Despite its simple implementation, performance of ODE was comprehensively proven using a large set of complex benchmark functions [24].…”

Section: A Experimentation Setupmentioning

confidence: 99%

“…In this experiment, we tested ODE at 60, 90, 120, 150 and 180 to investigate the results and found that the overall best results are obtained from PS = 60 and 90, and thus will only be reported in the next section. Other important parameters for ODE are as follows: CR = 0.85, F = 0.5, and jumping rate J R = 0.3, as recommended [24]. The abandon limit for both ABC and ABCDE is set to 0.6 PSD as recommended [11].…”

Section: A Experimentation Setupmentioning

confidence: 99%

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A Hybrid Artificial Bee Colony with Differential Evolution

Worasucheep¹

2015

IJMLC

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Abstract-Artificial bee colony (ABC) is a relatively new stochastic algorithm with competitive performance and minimal tuning parameter. This paper proposes a hybrid ABC algorithm with differential evolution (DE), but without additional parameters. DE is a well-known efficient evolutionary algorithm with proven records but its parameter setting is complicated. This proposed hybrid algorithm called ABCDE incorporates the powerful mutation strategies of DE into ABC, in order to increase convergence while diversity is not compromised. The performance of ABCDE is evaluated against both original ABC and opposition-based DE (ODE), a recent DE variant with high performance. The experiment uses twelve widely accepted non-linear benchmark functions with various characteristics, such as difficult landscape, multimodality, shift and rotation, to evaluate the ABCDE's performance on many complex functions. The experimental results demonstrate a superior performance of ABCDE against original ABC and ODE.

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“…Unlike the conventional evolutionary algorithms, which depend on predefined probability distribution function for mutation process, DE uses the differences of randomly sampled pairs of objective vectors. DE has drawn an increasing attention for a wide variety of engineering applications including power engineering applications such as transient stability, economic dispatch etc [14][15][16][17][18]. In this paper, a multi-objective methodology of DE based optimization has been proposed which along with the congestion and voltage security constraints, takes boundary conditions of generation cost into consideration.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution based Multi-objective Optimization of a Deregulated Power Network under Contingent State

Sen¹,

Chanda²,

Sengupta³

2011

ijeei

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This paper proposes a methodology to limit the variations in generation cost in a power system under normal and contingent state using Differential Evolution (DE) optimization technique keeping consumer welfare in view. The aim of this proposed methodology is to minimize the deviations of generation cost, during contingency, from a preferred value by rescheduling of the generation with a controlled load curtailment technique and hence relieving the lines from overloading for congestion management. A comparative study between rescheduling with and without load curtailment has also been presented in this paper. Numerical results on test system, namely IEEE 30 Bus System, are presented for illustration purpose and the same has been verified by a well esteemed optimization technique, Particle Swarm Optimization (PSO). The comprehensive simulation results establish that the developed method not only reduces the economic variations of the market but also ensures the voltage stability of the system during contingency.

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Opposition-Based Differential Evolution

Cited by 245 publications

References 47 publications

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

A new Metropolis optimisation method, the cross-section particle collision algorithm: some preliminary results

A Hybrid Artificial Bee Colony with Differential Evolution

Differential Evolution based Multi-objective Optimization of a Deregulated Power Network under Contingent State

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