Tuning metaheuristics: A data mining based approach for particle swarm optimization

Lessmann, Stefan; Caserta, Marco; Arango, Idel Montalvo

doi:10.1016/j.eswa.2011.04.075

Cited by 44 publications

(22 citation statements)

References 60 publications

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“…Metaheuristics have received increased popularity over the past decades due to their efficiency and effectiveness in complementing classical mathematical programming methods in solving large and complex problems in various applications [1]. Metaheuristic algorithms can be classified into two categories: trajectory based and population based.…”

Section: Introductionmentioning

confidence: 99%

Optimisation and rule firing analysis in fuzzy logic based maximum power point tracking

Letting

Munda

Hamam

2014

Journal of Intelligent &Amp; Fuzzy Systems

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This paper presents a technique for implementing population based metaheuristic algorithms during dynamic optimisation of a non-linear system with time varying inputs. The system dynamics due to the presence of multiple inputs and large signal variations are simulated when designing controller parameters. The proposed method is used to implement the particle swarm optimisation (PSO) algorithm and used to optimise fuzzy logic controllers for photovoltaic (PV) array maximum power point tracking. Controller optimisation is carried out using a large signal average model of the dc-dc converter. A rule firing analysis technique for interpretation of fuzzy logic controller rule participation at run-time is formulated. The rule inference parameters used for analysis are firing frequency, firing strength, and contribution to the control effort. The performance of optimised fuzzy logic controllers consisting of 9, 25, and 49 rules is analysed at run-time. Simulation results show that the fuzzy logic controller rules centred on the equilibrium point have the most significant contribution to the control effort. A fuzzy logic controller (FLC) with 9 rules can therefore give a good performance. The robustness of the 9-rule FLC is verified using the Lyapunov stability theory.

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Section: Introductionmentioning

confidence: 99%

Optimisation and rule firing analysis in fuzzy logic based maximum power point tracking

Letting

Munda

Hamam

2014

Journal of Intelligent &Amp; Fuzzy Systems

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“…Parameter tuning (e.g. tuning of cooling rate for the SA algorithm) has been found to be drastically influencing on both the efficiency and effectiveness of heuristics (Lessmann et al, 2011). Appropriate calibration of parameters boosts the capability of heuristic algorithms to find optimal or sub-optimal solutions in a rational amount of time (Akbaripour & Masehian, 2013) especially for problems with larger size.…”

Section: Tablementioning

confidence: 99%

Simultaneous selection and scheduling with sequence-dependent setup times, lateness penalties, and machine availability constraint: Heuristic approaches

Zarei¹,

Davvari²,

Kolahan³

et al. 2016

10.5267/j.ijiec

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Job selection and scheduling are among the most important decisions for production planning in today's manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs' processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time.

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“…For instance, there are works relying on fuzzy logic [31], support vector machine (SVM) [32], and linear and SVM regression [33]. 2.…”

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

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This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

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Tuning metaheuristics: A data mining based approach for particle swarm optimization

Cited by 44 publications

References 60 publications

Optimisation and rule firing analysis in fuzzy logic based maximum power point tracking

Optimisation and rule firing analysis in fuzzy logic based maximum power point tracking

Simultaneous selection and scheduling with sequence-dependent setup times, lateness penalties, and machine availability constraint: Heuristic approaches

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

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