Comparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function

Ismail, Idris; Halim, Abdul

doi:10.11591/ijece.v7i3.pp1643-1650

Cited by 13 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some effort in using TPO as optimization tool are applied in numerous application such as nonlinear ANFIS modeling [6], [10], and PID tuning [11]. In nonlinear optimization problems, TPO outperformed other metaheuristic algortihm with lesser computation time [12].…”

Section: Tree Physiology Optimizationmentioning

confidence: 99%

Tree Physiology Optimization in Constrained Optimization Problem

Halim¹,

Ismail²

2018

TELKOMNIKA

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Tree Physiology Optimizationmentioning

confidence: 99%

Tree Physiology Optimization in Constrained Optimization Problem

Halim¹,

Ismail²

2018

TELKOMNIKA

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the main characteristics of the CMA-ES is that it requires almost no parameter tuning for its application unlike most common heuristic optimization methods [20]. The choice of its internal parameters is not left to the user.…”

Section: Cma-es Algorithmmentioning

confidence: 99%

DNA Pool Analysis-based Forgery-Detection of Dairy Products

Rossi¹,

Modesto²,

Biolatti³

et al. 2018

IJECE

View full text Add to dashboard Cite

Food integrity and food safety have received much attention in recent years due to the dramatic increasing number of food frauds. In this article we focus on the problem of dairy products traceability. In particular, we propose an automatic forgery detection system able to detect frauds in milk and cheese. We investigate the use of Short Tandem Repeats analysis data, processed by a Covariance Matrix Adaptation Evolution Strategy algorithm in order to evaluate a traceability score between the products and their producer, and to highlight possible adulterations and inconsistencies. To demonstrate the usability of the proposed heuristic algorithm in a real setup, we also present the results collected from two real Italian farms.

show abstract

“…In contrast to conventional hard computing methods, SC exploits tolerant of imprecision, partial truth, uncertainty, robustness, and low-cost solutions [1]. Nowadays, SC algorithms have been successfully applied to many different problem-solving techniques [2]. One of them is the solution to the multi-peak global optimization problem that features multiple local peaks (LPs) and one global peak (GP).…”

Section: Introductionmentioning

confidence: 99%

New deterministic initialization method for soft computing global optimization algorithms

Hashim

Salam

Ismail

et al. 2020

IJEECS

View full text Add to dashboard Cite

<span>The initialization stage12 of a Soft Computing (SC) algorithm is vital as it affects the success rate of algorithms in solving multi-peak global optimization problems. The individuals in an initial population, which are known as search agents, are often generated randomly using pseudo-random number generator (PRNG) due to unavailability of prior information on the location of global peak (GP). The random nature of the generated search agents causes uneven distribution of the initial population over the search space (SS), which may lead the search towards unpromising regions from the very beginning. This paper proposes a new deterministic initialization method (DIM) for SC algorithms where search agents are evenly fixed in the SS by using a simple deterministic formulation. The performance of the proposed DIM is then compared to the conventional PRNG and more recent quasi-random number generator (QRNG). An optimization case study is carried out using two popular SC algorithms which are the Particle Swarm Algorithm (PSO) and the Evolutionary Programming (EP), and three relatively new SC algorithms which are the Whale Optimization Algorithm (WOA), the Elephant Herding Optimization (EHO), and the Butterfly Optimization Algorithm (BOA). The optimization is done on various one-dimensional (1D) benchmark functions, as well as practical problems such as partial shading condition (PSC). Simulation results show that the proposed DIM successfully improved the performance of each SC algorithm under study in solving almost all tested functions with 99% success rate compared to 88.7% and 80.2% for the QRNG and PRNG approaches respectively. Furthermore, the WOA is the most reliable and robust among the five SC techniques under study with a success </span>

show abstract

Comparative Study of Meta-heuristics Optimization Algorithm using Benchmark Function

Cited by 13 publications

References 12 publications

Tree Physiology Optimization in Constrained Optimization Problem

Tree Physiology Optimization in Constrained Optimization Problem

DNA Pool Analysis-based Forgery-Detection of Dairy Products

New deterministic initialization method for soft computing global optimization algorithms

Contact Info

Product

Resources

About