Mohammed a. Awadallah

Mohammed a. Awadallah
Al-Aqsa University · Faculty of Computers and Information Technology

Associate Professor

About

108
Publications
46,881
Reads
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2,609
Citations
Citations since 2017
74 Research Items
2292 Citations
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Introduction
Mohammed A. Awadallah received the PhD from the School of Computer Sciences at the Universiti Sains Malaysia (USM), Pulau Penang, Malaysia, in 2014. He is currently an Associate Professor with the Department of Computer and Information Sciences, Al-Aqsa University, P. O. Box 4051, Gaza, Palestine. His research interests on optimization methods (Artificial Bee Colony, Harmony Search, Bat, etc.) and combinatorial optimization problems like scheduling, rostering and timetabling problems.
Additional affiliations
June 2020 - present
Al-Aqsa University
Position
  • Professor (Associate)
January 2015 - May 2020
Al-Aqsa University
Position
  • Professor (Assistant)
March 2010 - July 2014
Universiti Sains Malaysia
Position
  • PhD Student

Publications

Publications (108)
Article
Full-text available
An Enhanced Chameleon Swarm Algorithm (ECSA) by integrating roulette wheel selection and Lé vy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm’s performance faces some hurdles, such as early convergence and slumping into l...
Article
The butterfly optimization algorithm (BOA) is a recent successful metaheuristic swarm-based optimization algorithm. The BOA has attracted scholars’ attention due to its extraordinary features. Such as the few adaptive parameters to handle and the high balance between exploration and exploitation. Accordingly, the BOA has been extensively adapted fo...
Article
Full-text available
The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is...
Article
This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer to tackle economic load dispatch (ELD) problems. ELD is an important problem in power systems that is tackled by finding the optimal schedule of the generation units that minimize fuel conceptions under a set of constraints. Due to the...
Article
Full-text available
In this paper, the learning process of multilayer perceptron (MLP) neural network is boosted using hybrid metaheuristic optimization algorithms. Normally, the learning process in MLP requires suitable settings of its weight and bias parameters. In the original version of MLP, the gradient descent algorithm is used as a learner in MLP which suffers...
Article
Full-text available
Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, si...
Article
Full-text available
In this paper, a modified version of the Multi-objective Grey Wolf Optimizer (MGWO), known as linked-based GWO (LMGWO), is proposed for the Appliances Energy Scheduling Problem (AESP). The proposed LMGWO is utilized by combining the MGWO searching mechanism with a novel strategy, called neighbourhood selection strategy, to improve local exploitatio...
Article
This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to tackle the blocking flow-shop scheduling problem. The island model is used to diversify the population and thus enhance the algorithm performance. The proposed method distributes the individuals in the population into different islands or sub-population. Then th...
Preprint
Full-text available
Economic Load Dispatch (ELD) is an important task in power systems that is concerned with scheduling a set of thermal generating units to produce specific power at low consumption costs. In this task, there are some operational constraints related to power demand, power output, valve-point loading effect, transmission losses, prohibited operating z...
Article
Full-text available
This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neu...
Article
In this paper, an enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed to deal with Feature Selection (FS) problems. FS is an important data reduction step in data mining which finds the most representative features from the entire data. Many FS-based swarm intelligence algorithms have been used to tackle FS. However, the door is st...
Article
Full-text available
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techni...
Article
Full-text available
In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality...
Article
This paper presents a novel meta-heuristic algorithm so-called White Shark Optimizer (WSO) to solve optimization problems over a continuous search space. The core ideas and underpinnings of WSO are inspired by the behaviors of great white sharks, including their exceptional senses of hearing and smell while navigating and foraging. These aspects of...
Article
Full-text available
The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimizati...
Article
In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversit...
Article
Full-text available
Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, meas...
Chapter
In this paper, the Travailing Salesman Problem (TSP) is tackled by Coronavirus Herd Immunity Optimizer (CHIO). TSP is the problem of finding the best tour for the salesman in order to visit all cities with minimum cost. In essential, this is a scheduling optimization problem that belongs to NP-hard class in almost all of its variants. CHIO is a rec...
Article
This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-s...
Article
Full-text available
Feature selection is an essential stage in many data mining and machine learning and applications that find the proper subset of features from a set of irrelevant, redundant, noisy and high dimensional data. This dimensional reduction is a vital task to increase classification accuracy and thus reduce the processing time. An optimization algorithm...
Poster
Full-text available
This conference is free of charge and published in IEEE. It is also a indexed by Scopus. Can find more details @ http://picict.ps/
Article
Full-text available
Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to find the best routes with minimum cost for a number of vehicles serving a number of scattered customers under some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routing problem, metaheuristic optimization algorithms...
Article
In this paper, the β-hill climbing optimizer is hybridized with the flower pollination algorithm (FPA) as a local refinement operator for global optimization problems. The proposed method is called HyFPAβ-hc. Such hybridization aims to enhance the balance between exploration and exploitation processes during the search, thus improving the quality o...
Article
Full-text available
In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristi...
Article
Full-text available
In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals...
Article
Full-text available
In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and \(\beta \)-hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the...
Article
DNA microarray technology is the fabrication of a single chip to contain a thousand genetic codes. Each microarray experiment can analyze many thousands of genes in parallel. The outcomes of the DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are widely applied to gene expression data to differentiate b...
Article
Full-text available
In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same clust...
Article
Full-text available
This paper proposes new versions of Harris Hawks Optimizer (HHO) incorporated the survival-of-the-fittest principle of evolutionary algorithms. HHO is the recent swarm-based optimization algorithm imitating the surprise pounce behaviour of Harris’ hawks chasing style. HHO can show different patterns of the exploration and exploitation. It has a sim...
Article
Full-text available
This paper proposes an efficient version of artificial bee colony (ABC) algorithm based on the island model concepts. The new version is called the island artificial bee colony (iABC) algorithm. It uses the structured population concept by applying the island model to improve the diversification capabilities of ABC. In the island model, the populat...
Article
In this paper, a new metaheuristic algorithm called JAYA algorithm has been adapted for feature selection. Feature selection is a typical problem in machine learning and data mining domain concerned with determining the subset of high discriminative features from the irrelevant, noisy, redundant, and high-dimensional features. JAYA algorithm is ini...
Experiment Findings
Full-text available
Article
Full-text available
Economic load dispatch (ELD) is a crucial problem in the power system which is tackled by distributing the required generation power through a set of units to minimize the fuel cost required. This distribution is subject to two main constraints: (1) equality and inequality related to power balance and power output, respectively. In the optimization...
Article
Full-text available
This paper presents an efficient method for aircraft landing problem (ALP) based on a mechanism that hybridizes the iterated local search (ILS) and simulated annealing (SA) algorithms. ALP is handled by scheduling each incoming aircraft to land on a runway in accordance with a predefined landing time frame. The main objective to address is to find...
Preprint
Full-text available
In this paper, a new nature-inspired human-based optimization algorithm is proposed which called Coronavirus Herd Immunity Optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals dir...
Chapter
Full-text available
At the present time, 15% of the growing world population is estimated to have disabilities and special needs. Disabilities can seriously limit participation in regular life activities, such as controlling home facilities, using transportation services, joining social events, accessing educational contents, to name but a few. With the advancement in...
Article
The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuris...
Article
Full-text available
Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms simulate the foraging behavior of honey bees in their hive. ABC starts with a colony of artificial bees with sole aim of discovering the place of food sources with high nectar amount using the solution search equation in the employed bee and onlooker...
Article
Full-text available
Flower pollination algorithm (FPA) is a recent swarm-based evolutionary algorithm that was inspired by the biological evolution of pollination of the flowers. It deals with a panmictic population of pollens (or solutions) at each generation, using global and local pollination operators, to improve the whole population at once. Like other evolutiona...
Article
In this paper, an adaptive version of \(\beta -\)hill climbing is proposed. In the original \(\beta -\)hill climbing, two control parameters are utilized to strike the right balance between a local-nearby exploitation and a global wide-range exploration during the search: \({\mathcal {N}}\) and \(\beta \), respectively. Conventionally, these two pa...
Preprint
Full-text available
We consider translation surfaces in the 3-dimensional Euclidean space which are of coordinate finite type with respect to the third fundamental form $III$, i.e. their position vector $x$ satisfies the relation $\Delta^{III}x = \Lambda x$, where $\Lambda$ is a square matrix of order 3. We show that Sherk's minimal surface is the only translation sur...
Article
Full-text available
This paper proposed a new gene selection method based on modified Minimum Redundancy Maximum Relevancy (MRMR) as a filtering approach and hybrid bat algorithm with β-hill climbing as an efficient wrapper approach. The gene selection is a process of selecting the discriminative genes that aid in the development of efficient cancer diagnosis and clas...
Article
The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation. In contrast, when the selection has a tendency to be r...
Poster
Good News! Based on many requests, the deadlines have been extended as follows: Draft paper submission: June 22, 2018 Author decision notification: August 1, 2018 Final paper submission: September 15, 2018 Conference date: 3-4 October 2018 https://www.icpet.net/important-dates
Article
Full-text available
In this paper, the update process of harmony search (HS) algorithm is modified to improve its concept of diversity. The update process in HS is based on a greedy mechanism in which the new harmony solution, created in each generation, replaces the worst individual in the population, if better. This greedy process could be improved with other update...
Poster
Full-text available
ICPET 2018 will host researchers and technologists from industry and academia in Palestine and all over the world to present their latest research results in fields where computer, electronic and electrical engineering can be applied. The audience of the conference and the participants will be university professors, graduate students, professional...
Article
The patient admission scheduling (PAS) problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmony search (HS) algorithm. We tailor the HS to solve...
Article
Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterat...
Article
In this paper, the problem of economic load dispatch (ELD) is tackled using a recently introduced local search-based method called \(\beta \)-hill climbing optimizer. In a power system, the ELD problem is tackled by arranging a set of generation units’ outputs in a specific order to minimize the cost of the operating fuel and to match the power sys...
Chapter
Full-text available
The flower pollination algorithm (FPA) is a nature-inspired algorithm that imitates the pollination behavior of flowering plants. Optimal plant reproduction strategy involves the survival of the fittest as well as the optimal reproduction of plants in terms of numbers. These factors represent the fundamentals of the FPA and are optimization-oriente...
Article
Full-text available
This paper introduces βHCWT, a hybrid of the β-hill climbing metaheuristic algorithm and wavelet transform (WT), as a new method for denoising electrocardiogram (ECG) signals. ECG signals are non-stationary signals that provide a graphical measure of electrical activities in human heart muscles. However, given their non-stationarity, these signals...
Article
Full-text available
The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and...
Article
Full-text available
We consider translation surfaces in the 3-dimensional Euclidean space which are of coordinate finite type with respect to the third fundamental form III, i.e. their position vector x satisfies the relation ∆ III x = Λx, where Λ is a square matrix of order 3. We show that Sherk's minimal surface is the only translation surface satisfying ∆ III x = Λ...