Differential evolution algorithm with fitness and diversity ranking-based mutation operator

Cheng, Jianchao; Pan, Zhibin; Liang, Hao; Gao, Zhaoqi; Gao, Jinghuai

doi:10.1016/j.swevo.2020.100816

Cited by 76 publications

(43 citation statements)

References 36 publications

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“…for each target vector, the equation that is used to construct these mutant vectors is as follows [1][2][3][4][5]; ). On the rarest occasion, a part of the mutant vector violates the limits of the search space.…”

Section: Mutationmentioning

confidence: 99%

“…When 1 or more values in the target vector are crossed with 1 or more values in the mutated vector in the crossover process, the target vector's values are recombined to yield the trial vector [1][2][3][4][5]…”

Section: Crossovermentioning

confidence: 99%

“…𝑉 �⃗ 𝑗,𝑖,g is set to 𝑉 �⃗ 𝑗,𝑖,g+1 , If the trial vector 𝑉 �⃗ 𝑗,𝑖,g gives a optimum fitness function than X � �⃗ 𝑅 1 𝑖 g, Otherwise, the old vector X � �⃗ 𝑅 1 𝑖 ,g is held. The choice plan is as per the following equation [1][2][3][4][5];…”

Section: Selectionmentioning

confidence: 99%

“…MOSADE was compared to GDE, and the given result showed that it performs well in diversity and convergence rate. In order to measure the quality of the individual and improve the diversity, Cheng et al [1] proposed a differential development algorithm in 2021. The proposed approach that was being developed included an algorithm called FDDE which had a fitness and diversity ranking-based mutation operator.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems

et al. 2021

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Simultaneous optimization of two or more objectives is an instance of multi-objective optimization (MOO). However, for most of the Multi-Objective Problems (MOPs), no single solution can optimize all the objective functions simultaneously. Evolutionary algorithms are a type of optimization algorithm used for constructing a well-distributed optimal front more quickly than a much more efficient approach. One of the most commonly used algorithms in this regard is differential evolution (DE). To solve the problem of non-dominated sorting for Multi-Objective DE (MODE), an approach is proposed that reduces the time complexity. DE is commonly recognized as one of the best methods for solving the MOPs. Several versions of DEs have been proposed for MOPs. This article proposes a novel mutation technique, named Environmental Parameter-based Multi-Objective Differential Evolution (EP-MODE) that acquires two additional environmental parameters to preserve diversity and accelerate the convergence. The proposed approach also reduces the time complexity of non-dominated sorting for Multi-Objective DE (MODE). The proposed mutation's performance has been evaluated on DTLZ series and ZDT series benchmark test functions on the Pareto-optimal front and compared with existing multi-objective algorithms (MODE and MODE-RMO). The solution comparatively verifies that the proposed EP-MODE outperforms most of the MODEs for lower dimension functions and higher functions. HIGHLIGHTS New environmental parameters-based mutation has been introduced in a differential evolution algorithm to solve multi-objective optimization problems (EP-mode) The addition of two additional environmental parameters to EP-mode has been found to be appropriate for maintaining diversity while also accelerating the convergence rate EP-MODE evaluated on bi-objective (DTLZ series) and tri-objective (ZDT series) benchmark test functions on the Pareto-optimal front and found EP-MODE's efficiency is higher than that of the MODE algorithm and the MODE algorithm with a ranking-based mutation operator GRAPHICAL ABSTRACT

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Section: Mutationmentioning

confidence: 99%

Section: Crossovermentioning

confidence: 99%

Section: Selectionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems

et al. 2021

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“…Tian et al [ 24 ] presented a DE with improved individual-based parameter setting and selection strategy (IDEI), which developed a diversity selection strategy based on the newly defined weighted fitness value. Cheng et al [ 25 ] proposed an improved DE with fitness and diversity ranking-based mutation operator (FDDE), which judged the contribution of individuals participating in the "DE/rand/1" mutation strategy to population diversity according to their fitness values, and rearranged the positions of the three random individuals based on the ranking information of individual diversity and fitness values.…”

Section: Introductionmentioning

confidence: 99%

A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

et al. 2021

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Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

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Optimal Dispatch of Water Source Heat Pump Regional Energy Systems Based on Neighborhood Adaptive Particle Swarm Optimization

Wang

Zhang

et al. 2022

Energy Tech

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To achieve the global Carbon Neutral goal required by the Paris Agreement, considerable concerns are advanced regarding improving the efficacy of clean energy in recent years. Taking as the research object a ground‐source heat pump (GSHP), which is a recently developed, highly efficient, and energy‐saving air‐conditioning technology, herein, the optimization of operational strategies regarding GSHP systems from the perspective of its individual heat pump units is studied. To attain this aim, an optimal scheduling model of surface water source heat pump regional energy systems with energy storage devices is established and then solved by the neighborhood adaptive particle swarm optimization (NAPSO) algorithm proposed. A method is proposed to transform the mixed integer programming problem into a continuous optimization problem to address the energy storage device model difficulties with integer variables. The experimental results show that the NAPSO algorithm can save 8.65% in cooling conditions and 7.22% in heating conditions in terms of operating costs compared with the empirical operation method. In addition, it can save 11.13% and 9.66% of operating expenses on average compared with the five other intelligent optimization algorithms.

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Differential evolution algorithm with fitness and diversity ranking-based mutation operator

Cited by 76 publications

References 36 publications

Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems

Multi-Objective Differential Evolution Algorithm with a New Environmental Parameter based Mutation for Solving Optimization Problems

A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

Optimal Dispatch of Water Source Heat Pump Regional Energy Systems Based on Neighborhood Adaptive Particle Swarm Optimization

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