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Cited by 125 publications
(43 citation statements)
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References 47 publications
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“…Tseng and Chen [28] presented an MA by combining GA with a local search method to solve a multimode resource-constrained project scheduling problem (RCPSP). To solve the real-time multirobot path-planning problem, Rakshit et al [29] proposed an adaptive MA by using differential evolution for global search and Q-learning for local refinement. Mei et al [30] proposed an MA for the periodic capacitated arc routing problem, where a route-merging procedure was devised and embedded to tackle the insensitive objective.…”
Section: B Literature On Memetic Algorithms and Edamentioning
confidence: 99%
“…Tseng and Chen [28] presented an MA by combining GA with a local search method to solve a multimode resource-constrained project scheduling problem (RCPSP). To solve the real-time multirobot path-planning problem, Rakshit et al [29] proposed an adaptive MA by using differential evolution for global search and Q-learning for local refinement. Mei et al [30] proposed an MA for the periodic capacitated arc routing problem, where a route-merging procedure was devised and embedded to tackle the insensitive objective.…”
Section: B Literature On Memetic Algorithms and Edamentioning
confidence: 99%
“…Differential Evolution for Multi-objective Optimization (DEMO) [35] employs an evolutionary strategy that utilizes the advantages of Differential Evolution (DE) [33] with the mechanisms of Pareto dominance based ranking and crowding distance sorting. An overview of the main steps of the DEMO algorithm is presented next.…”
Section: Differential Evolution For Multi-objective Optimizationmentioning
confidence: 99%
“…DEMO [8] employs an evolutionary strategy that utilizes the advantages of the differential evolution [22] with the mechanisms of Pareto-based ranking and crowding distance sorting. An overview of the main steps of the DEMO algorithm is presented next:…”
Section: Differential Evolution For Moomentioning
confidence: 99%