A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems

Sallam, Karam M.; Chakrabortty, Ripon K.; Ryan, Michael J.

doi:10.1016/j.future.2020.02.074

Cited by 41 publications

(15 citation statements)

References 37 publications

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“…In order to optimize the initial solutions, two most popular mutation strategies are employed in a self-adaptive fashion (discussed in Section 3.2.2). Thereafter, the widely-used binomial crossover [14] with a crossover factor C r is applied to produce an offspring population. To convert the generated infeasible solutions that violate the constraint boundaries, a repair mechanism is therefore designed, outlined in Algorithm 3.…”

Section: Seed Optimizationmentioning

confidence: 99%

“…At first, a continuous value against each candidate node is generated using rand(0,1) function, which represents a continuous solution in the initial population. Afterwards, the Largest-Ranked-Value (LRV) [14] technique is used to convert this continuous solution into a discrete solution to select top-k nodes with largest values as the seed set. An example of the solution representation system of our proposed SAW-DE algorithm is given in Fig.…”

Section: Solution Representation and Initializationmentioning

confidence: 99%

“…The application of SAW is not sufficient to identify the optimal solution, as it does not consider the overlapping influence. Therefore, we design an optimization framework based on DE, which is the most advanced and powerful meta-heuristic of Evolutionary class [14] for finding the optimal seed set. The relative benefits of a problemindependent meta-heuristic over a problem-specific heuristic/approximation algorithm are [15]: (i) they are easy to design and implement without knowing much about the considered problem, and (ii) due to their stochastic search, they are capable of producing diverse solutions and escaping from local optima.…”

Section: Introductionmentioning

confidence: 99%

“…As there is no known single search operator that performs well for wide-range of problem instances, our proposed approach leverages the strengths of two mutation operators in a self-adaptive manner. As the performance of a meta-heuristic algorithm greatly depends on its parameters and finding their optimal values are not an easy task [14], we also adopt the self-adaptive strategies for most of them.…”

Section: Introductionmentioning

confidence: 99%

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Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

Biswas¹

2021

Preprint

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The Influence Maximization (IM) problem aims at maximizing the diffusion of information or adoption of products among users in a social network by identifying and activating a set of initial users. In real-life applications, it is not unrealistic to have a higher activation cost for a user with higher influence. However, the existing works on IM consider finding the most influential users as the seed set, ignoring either the activation costs of such individual nodes and the total budget or the size of the seed set, which may not be always an optimal solution, particularly from the financial and managerial perspectives, respectively. To address these issues, we propose a more realistic and generalized formulation termed as multi-constraint influence maximization (MCIM) aiming to achieve a cost-effective solution under both budgetary and cardinality constraints. Unlike the existing IM formulations, the proposed MCIM is no longer a monotone but a submodular function. As it is also proved to be an NP-hard problem, we propose a simple additive weighting (SAW) assisted differential evolution (DE) algorithm for solving the large-size real-world problems. Experimental results on four real-world datasets show that the proposed formulation and algorithm are effective in finding a cost-effective seed set.

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Section: Seed Optimizationmentioning

confidence: 99%

Section: Solution Representation and Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

Biswas¹

2021

Preprint

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“…An improved variant of a multioperator DE algorithm was also proposed by Sallam et al [35], in which three mutation strategies and two crossover operators have been used, with two indicators, the diversity of population and quality of solutions, are used to automatically select the better-performing operator during the evolutionary task, which has been adapted to solve realworld constrained optimization problems [45]. A two-stage MODE, called TS-MODE, was recently proposed to solve combinatorial optimization problems [46], in which the first stage is responsible for exploration while the second one is responsible for exploitation.…”

Section: ) Multi-operator De Variantsmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

Self Cite

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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Investing the Application of Resource-Constrained Project Scheduling Problem in a Single-Mode Construction Project

Devagekar

Balasubramanian

2022

Lecture Notes in Civil Engineering

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A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems

Cited by 41 publications

References 37 publications

Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

Selecting a Cost-Effective Seed for Maximizing the Social Influence Under Real-life Constraints

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

Investing the Application of Resource-Constrained Project Scheduling Problem in a Single-Mode Construction Project

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