Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

Adekanmbi, Oluwole; Green, Paul

doi:10.1155/2015/936106

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…It is observable that the DeLOCP with the mutation strategy of DE/Target-to-Best/1 and Hybrid DE/Rand/1 and DE/Best/1 have produced the best solution: fitness function = 3054 (with average labor demand = 13.8, maximum labor demand = 16.0, minimum labor demand = 12, and project duration = 32.0 (shift)). The optimized crew sizes and start times of all activities are [13,7,4,9,6,4,6,7,8,8,13] and [1,2,2,6,14,9,9,25,27,17,32], respectively. The optimized daily labor demand is illustrated in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

“…Based on the literature review, it is recognizable that employing metaheuristic approaches to solve complex engineering problems has been a major trend in the research community [11][12][13][14][15][16][17]. Among metaheuristic approaches, the Differential Evolution (DE) [18] has received an increasing attention and this algorithm has been applied in a wide span of problem domain [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Hoang

Nguyen

Pham

2015

Advances in Civil Engineering

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In construction management, the task of planning project schedules with consideration of labor utilization is very crucial. However, the commonly used critical path method (CPM) does not inherently take into account this issue. Consequently, the labor utilization of the project schedule derived from the CPM method often has substantial low ebbs and high peaks. This research proposes a model to obtain project schedule with the least fluctuation in labor demand while still satisfying the project deadline and maintain the project cost. The Differential Evolution (DE), a fast and efficient metaheuristic, is employed to search for the most desirable solution of project execution among numerous combinations of activities’ crew sizes and start times. Furthermore, seven DE’s mutation strategies have also been employed for solving the optimization at hand. Experiment results point out that theTarget-to-Best 1and a new hybrid mutation strategy can attain the best solution of project schedule with the least fluctuation in labor demand. Accordingly, the proposed framework can be an effective tool to assist decision-makers in the project planning phase.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Hoang

Nguyen

Pham

2015

Advances in Civil Engineering

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“…The algorithm with a higher score is denoted as poorer compared to the lower ratio. Examples such as Adekanmbi and Green (2015) and Lee et al (2019a) of best to worst solution as an indicator of algorithm improvement in engineering optimization and water distribution problems. Another example is Santos et al (2019) in the combinatorial Bin-packing problem that evaluates the ratio between the best and worst solutions of total bins.…”

Section: 216mentioning

confidence: 99%

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

2020

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The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this advantage, the stochastic nature of such algorithms resulted in a spectrum of solutions by a certain number of trials that may lead to the uncertainty of quality solutions. Therefore, it is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives. The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm's speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm's capability of finding quality solutions. Both scopes are crucial for continuous and discrete problems either in single-or multi-objectives. Each problem type has different formulation and methods of measurement within the scope of efficiency and effectiveness performance. One of the most decisive verdicts for the effectiveness measure is the statistical analysis that depends on the data distribution and appropriate tool for correct judgments.

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“…There are other SI-based metaheuristics which are inspired by physical and chemical systems, such as the gravitational search algorithm (24). All swarm intelligence metaheuristics are population-based and composed of simple agents interacting with each other and the environment following simple rules, which lead to an intelligence global behavior (25). A number of SI-based metaheuristics have been proposed and they have shown superior skills in solving various optimization problems (12, 14-16, 26, 27).…”

Section: Swarm Intelligence-based Metaheuristicsmentioning

confidence: 99%

Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics

Yasear¹

2019

Baghdad Sci.J

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Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory characteristics and memory in SI-based metaheuristics. The latest information and references have been further analyzed to extract key information and mapped into respective subsections. A total of 50 references related to memory usage studies from 2003 to 2018 have been investigated and show that the usage of memory is extremely necessary to increase effectiveness of metaheuristics by taking the advantages from their previous successful experiences. Therefore, in advanced metaheuristics, memory is considered as one of the fundamental elements of an efficient metaheuristic. Issues in memory usage have also been highlighted. The results of this review are beneficial to the researchers in developing efficient metaheuristics, by taking into consideration the usage of memory.

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Conceptual Comparison of Population Based Metaheuristics for Engineering Problems

Cited by 10 publications

References 32 publications

Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Optimizing Construction Project Labor Utilization Using Differential Evolution: A Comparative Study of Mutation Strategies

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics

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