Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation

Novitasari, Dinda; Cholissodin, Imam; Mahmudy, Wayan Firdaus

doi:10.12928/telkomnika.v14i1.2812

Cited by 22 publications

(17 citation statements)

References 18 publications

(16 reference statements)

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“…Based on [29] and [30], the heuristic and stochastic method always generate a different solution in each run. Therefore, in this study, the testing is run ten times.…”

Section: Resultsmentioning

confidence: 99%

Hybrid Modified Evolution Strategies and Linear Programming for Beef Cattle Feed Optimization

Fatyanosa¹,

Mahmudy²,

Marjuki³

2018

IJEEI

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The biggest obstacle faced by cattleman in cattle fattening is the high feed cost. Cattleman has to formulate cattle feed that meets the nutrient requirements of their cattle and minimize the feed costs. These problems belong to a class of constrained optimization. Various heuristic and deterministic algorithms have been used for solving the constrained optimization problems and applied to the feedstuffs composition. However, there is still some instability in finding mainly a reliable technique that can steadily discover solutions, which are truly near the global optimum of the problem. This paper proposes a combined Modified Evolution Strategies (MES) and Linear Programming (LP) method, named MESLP for optimizing the beef cattle feed. From this study, this novel method obtained a better result than either LP, Random Search, Genetic Algorithm, or Evolution Strategies with higher fitness and lower price.

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“…Based on [29] and [30], the heuristic and stochastic method always generate a different solution in each run. Therefore, in this study, the testing is run ten times.…”

Section: Resultsmentioning

confidence: 99%

Hybrid Modified Evolution Strategies and Linear Programming for Beef Cattle Feed Optimization

Fatyanosa¹,

Mahmudy²,

Marjuki³

2018

IJEEI

Self Cite

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“…The optimization algorithms previously used to solve this design optimization problem include GA3, 56 GA4, 35 APSO, IAPSO, MBA, LCA, WCA, DE, SC, NM-PSO, PSO-DE, HPSO, 29 CPSO, 24 CAEP, GA1, hybrid PSO-GA (HPSO), 39 ABC2, 40 and GA2. Table 5 presents the comparison of optimal solutions provided by the previously reported algorithms and proposed DAPSO-GA. From Table 5, a new optimal solution, which is better than those provided by previously proposed algorithms, is found by the proposed DAPSO-GA with the objective function value of 1.6600473.…”

Section: DVmentioning

confidence: 99%

A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

Zhu

2019

Advances in Mechanical Engineering

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A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

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“…The firefly with less brightness will be attracted by the brighter one. The intensity I can be expressed as follow: (2) Where dim is the dimension of x i , x ik is the kth component of x i .…”

Section: Chaotic Firefly Algorithm 21 Original Version Of Famentioning

confidence: 99%

“…These techniques attempt to mimic some characteristics of natural phenomena or social behavior [1,2]. Metaheuristic algorithms are simple and easy to be implemented.…”

Section: Introductionmentioning

confidence: 99%

Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch

et al. 2017

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Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation

Abstract: Abstract

Cited by 22 publications

References 18 publications

Hybrid Modified Evolution Strategies and Linear Programming for Beef Cattle Feed Optimization

Hybrid Modified Evolution Strategies and Linear Programming for Beef Cattle Feed Optimization

A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive Power Dispatch

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