Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms

Keller, André A.

doi:10.2174/97816810870541190101

Cited by 12 publications

(5 citation statements)

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“…SA establishes a connection between the thermodynamic process and the search for global optimum in optimization problems. Within the SA process there are three key components: cooling equation, acceptance criteria, and generation mechanisms [29,31].…”

Section: Simulated Annealingmentioning

confidence: 99%

“…There are three common basic types of cooling equations: linear, geometric, and exponential. Geometric cooling schedules are most widely used in practice [31]. As such, it will also be employed by this work.…”

Section: Cooling Equationmentioning

confidence: 99%

“…Let ∆E ≡ J(I) − J( Ī) and let f (•) be the function that describes the probability of accepting a candidate solution Ī. The probability of accepting a candidate solution is thus of the form [31]…”

Section: Acceptance Criteriamentioning

confidence: 99%

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A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

Brown,

Droge

2024

Preprint

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With an increasing adoption of Battery Electric Bus (BEB) fleets, developing a reliable charging schedule is vital to a successful migration from their fossil fuel counterparts. In this paper, a Simulated Annealing (SA) implementation is developed for a charge scheduling framework for a fixed-schedule fleet of BEBs that utilizes a proportional battery dynamics model, accounts for multiple charger types, allows partial charging, and further considers the total energy consumed by the schedule as well as peak power use. Two generation mechanisms are implemented for the SA algorithm denoted as the "quick" and "heuristic" implementations, respectively. The model validity is demonstrated by utilizing a set of routes sampled from the Utah Transit Authority (UTA) and comparing the results against two other models: the BPAP and the Qin-Modified. The results presented show that both SA techniques offer a means of generating operationally feasible schedules quickly while minimizing the cost of operation and considering battery health.

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Section: Simulated Annealingmentioning

confidence: 99%

Section: Cooling Equationmentioning

confidence: 99%

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A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

Brown,

Droge

2024

Preprint

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“…Generally, the common stages in solving the MOO problem are to first calculate the Pareto-optimal front of non-dominated solutions [10], rank them, and select one by a decision-making process [11]. Such a solution can improve some of the design objectives and deteriorate others.…”

Section: Hubmentioning

confidence: 99%

A novel concept for multi-objective optimization of composite power transmission shafts

Maalawi,

Nasr,

EL-Bayoumi

2024

J Braz. Soc. Mech. Sci. Eng.

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“…Figure 1 shows a particular case of the Pareto front in the presence of two-objective functions. Several techniques have been proposed to solve MOOP [43]. This work is based on an evolutionary algorithm, which has shown advantages over classical techniques.…”

Section: Multi-objective Optimization Problemmentioning

confidence: 99%

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

Aguilera-Rueda

Cruz-Ramírez

Mezura-Montes

2020

MCA

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We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is based on the well-known NSGA-II algorithm. The core idea is to reduce the implicit selection bias-variance decomposition while identifying a set of competitive models using both objectives. Numerical results suggest that, in stark contrast to the single-objective approach, our bi-objective approach is useful to find competitive Bayesian networks especially in the complexity. Furthermore, our approach presents the end user with a set of solutions by showing different Bayesian network and their respective MDL and classification accuracy results.

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Multi-Objective Optimization In Theory and Practice II: Metaheuristic Algorithms

Cited by 12 publications

References 0 publications

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

A Simulated Annealing Approach to the Scheduling of Battery-Electric Bus Charging

A novel concept for multi-objective optimization of composite power transmission shafts

Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

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