Bio-inspired metaheuristic algorithms and the Quran

First International Engineering Conference (IEC2014)

Bio-inspired metaheuristic algorithms and the Quran: Relevance & justifications Rosni Abdullah1, Ali Kattan2 School of Computer Sciences, USM, Penang, Malaysia 1 [email protected] IEEE Member, IT Department, Ishik University, 100 Meter St., Erbil, Iraq 2 [email protected]

ABSTRACT Bio-inspired metaheuristics are now commonly used to solve many optimization problems and have proved to be effective optimization techniques. Such stochasticbased techniques are being deployed in many evolutionary algorithms. Swarm intelligence is among the mostly used techniques in optimization methods. Common examples include ant colony optimization and bee colony optimization. Such techniques are preferred over traditional numerical-based methods. The “inspiration” that lead to the invention and development of such techniques is based on observations from nature. In this work, the authors show the relevance of some of the existing and most commonly used bio-inspired algorithms and verses from the Holy Quran. By justifying such relevance, the paper poses the question of whether the Holy Quran could be used as source of such “inspiration” for yet to be invented meta-heuristics? Keywords: Swarm Optimization, Quran.

Intelligence,

Metaheuristics,

Evolutionary

Algorithms,

1. INTRODUCTION An evolutionary algorithm (EA) is a population-based metaheuristic optimization algorithm. EAs are inspired by biological evolution. Swarm Intelligence (SI) falls under EAs where such expression was first introduced in 1989 in the context of cellular robotic systems [1]. An SI system would typically consist of a population of agents interacting locally with one another and the environment they are in. The collective social behavior (or organisms) forms the bases of SI: “It encompasses the implementation of collective intelligence of groups of simple agents that are based on the behavior of real world insect swarms, as a problem-solving tool.” [2]. The inspiration would usually come from nature, especially biological systems and hence the name bio-inspired. Common examples include ant colonies, bee colonies bird flocking, animal herding, bacterial growth, and fish schooling [3-6]. SI has attracted extensive attention in various research areas being an innovative computational and behavioral metaphor for solving many engineering optimization problems. Many studies have reported success using such techniques for solving difficult problems [2]. By converting the engineering problem into optimization problem, a solution can be obtained [7, 8].

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In Islamic religion, the main belief is that Allah Almighty, has provided the laws and regulations comprising belief, laws and moral system that are suitable to be implemented by mankind to govern their lives [9]. The Holy Quran being the primary source of such knowledge and Muslims believe that each Islamic practice has its advantages and benefits in many different ways [10]. In this work the authors show the relevance of some of the existing and most commonly used SI-based algorithms and verses from the Holy Quran. Two widely used bio-inspired metaheuristics are considered: particle swarm optimization (PSO) and ant colony optimization (ACO). By justifying such relevance, the paper poses the question of whether the Quran could be used as a source of such “inspiration” for yet to be invented metaheuristics. Many verses of Quran are cited along with their English translation. Being aware of the many subtle issues in terms entity-coherence and lexical cohesion analysis on English translations of Quran that include differences in word domain, structure selection and word and phrase ordering are some of the common issue [11-13], an extra effort was taken1 to provide a more concise explanation considering certain measures [14]2. This rest of this paper is organized as follows: Section 2 gives a brief background about metaheuristics and their concept including three common SI techniques; namely PSO and ACO. Section 3 presents several verses from the Holy Quran to justify it as a source of knowledge and wisdom. Section 4 comprises the bulk of this work by showing the relevance and justifications of some of the common bioinspired techniques and verses from the Holy Quran. Finally the conclusions are given in section 5. 2. BACKGROUND An SI-based techniques is generically named "metaheuristic"; a search technique that is structured on biological and physical phenomena [15]. Such metaheuristics usually make few assumptions about the optimization problem considered making them commonly used methods for solving variety of optimization problems [16-19]. A metaheuristic is a higher-level procedure or heuristic designed such that it can select a lower-level procedure or heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect data or limited computation capacity.[7]. All of these metaheuristics are stochastic-based, i.e. probabilistic; the rules of randomness are combined to imitate the process that inspired the algorithm [20]. The general optimization concept of evolutionary metaheuristics algorithms is shown in Figure 1.

1 2

The 2nd author is a native speaker of Arabic. The translations associated with the cited Quran verses in this paper are based on Shakir’s translation attributed to Muhammad Habib Shakir, (1866, Cairo–1939, Cairo).

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FIGURE 1. Evolutionary algorithm concept PSO is an SI technique that is inspired from the social behavior of bird flocking in the process of searching for food. It was first proposed by Kennedy and Eberhart in 1995 [6]. Each individual within the swarm is referred to as particle and is subject to velocity and acceleration towards a better mode of behavior. The concept is that each particle in the swarm represents a solution in a high-dimensional space having four vector quantities: current position, best position found so far, the best position found by its neighborhood so far and its velocity. The position is to be adjusted in the search space based on the best position reached by itself and on the best position reached by its neighborhood during the search process [2, 4]. This solution sequence is adopted whereby each particle in the swarm performs local search (learning based on their own experiences) and global search (learn from the experiences of the group). ACO is another SI technique that has been used for solving computational problems. ACO was initially proposed by Marco Dorigo in 1991 [21], which was also his main work for PhD. The algorithm aims to search for an optimal path in a graph based on the behavior of ants seeking a path between their colony and a source of food. An illustration is shown in Figure 2. ACO was inspired by the foraging behavior of actual ant colonies. When ants search for food, they would initially perform a random exploration process of the area surrounding their nest. Ants would leave a chemical pheromone trails on the ground as they move. Once an ant finds a food source, the quantity and the quality of the food is evaluated and some are carried back to the nest. In the trip back to the nest, the ant would leave a pheromone trail with quantity that may depend on the quantity and quality of the food discovered. This trail would guide other ants to the discovered food source. The indirect communication between the ants via pheromone trails enables them to find shortest paths between their nest and food sources and this what is being exploited in artificial ant colonies for solving optimization problems [23]. Based on the flowchart given earlier in Figure 1, ACO works by initializing the solution archive (population). Then, at each iteration a number of solutions are stochastically constructed computing its fitness based on a heuristic measure. Finally, the solution archived is updated with the generated solutions and the process continues until terminated when certain conditions are met [22, 23]. The original idea behind ACO has been diversified to solve a wider range of numerical problems, and as a result, many enhanced and modified versions of the original algorithm have been used to solve a variety of optimization problems [24].

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FIGURE 2. ACO concept for finding the shortest path. The pheromone trails are shown as dashed lines whose thickness indicates the trails’ strength [22]. 3. THE QURAN AS SOURCE OF KNOWLEDGE AND WISDOM Muslim believers consider Islam as a complete, dynamic and holistic religion in which everything that happens definitely can be handled by Islamic ethics properly guided by verses from al-Quran, quotations from Sunnah and other Islamic sources [10, 25]. Figure 3 shows verse 54 of Chapter 18, Al-Kahaf, which states the concept of “every kind of example”. In verse 99 of Chapter 2, Al-Bakara, and verse 37 of Chapter 10, Younis, both shown in Figure 4 and Figure 5 respectively, Allah almighty, indicates that these verses (of Holly Quran), are clear and no one can deny and Quran produced by Allah’s almighty, the one and only. These verses, in addition to many others, portray the value of Quran as a source of knowledge and wisdom. A more intellectual form are required from Muslim believers and as given in verse 44 of Chapter 16, Al-Nahal, shown in Figure 6. At the end of this verse it is mentioned, “give thought”. This phrase actually corresponds to a single verb in the original Arabic script of Quran. It refers to mankind to think, intellectually, of what the examples and proofs given in Quran in order to believe. Allah Almighty is addressing mankind to think and then consider. This is where it is clearly stated that “thinking” and “considering” is a means to reach “belief”. The Holy Quran offers several detailed accounts of specific historical events with the aim of emphasizing the moral significance of an event over its narrative sequence. Considering verse 54 of Chapter 18, Al-Kahaf, given in Figure 3, the only disputers of the value of Quran in the context presented, is mankind, in this case disobedient or disbelievers as indicated in verse 99 of Chapter 2, Al-Bakara, shown in Figure 4.

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FIGURE 3. Verse 54 of Chapter 18, Al-Kahaf.

FIGURE 4. Verse 99 of Chapter 2, Al-Bakara.

FIGURE 5. Verse 37 of Chapter 10, Younis.

FIGURE 6. Verse 44 of Chapter 16, Al-Nahal.

4. RELEVANCE & JUSTIFICATIONS The SI metaheuristics presented in section 2 were inspired from the behavior of social insects, like ants and honeybees as wall as swarming, such as birds. In the previous section the rationale behind adopting the Quran as source of knowledge and wisdom was discussed based on verses from Quran. Astonishingly, the inspiration behind many of the concepts used by the aforementioned SI techniques is already cited in the Quran. In fact, two chapters of the Holy Quran, namely Chapter 27, AlNamil and Chapter 16, Al-Nahal, are named after the two social swarms behind those SI metaheuristics, namely “ants” and “bees”. Let us start by looking at verse 38 of Chapter 6, Al-Ana’am shown in Figure 7. In this verse it is indicated explicitly that creatures on earth or birds are communities (i.e. populations) like mankind. The translated “you” in this verse refers to plural indicating mankind. Consequently, it implicitly indicates that they have the “intelligence” to communicate just like mankind. Birds, a known social swarm, is mentioned again in two verses of Chapter 27, Al-Namil; verse 16 and 17, are given in Figure 8 and 10 respectively. These creatures were selected to assist Solomon as his solders. The interesting part in these two verses is the plural form of the word bird noting that it was mentioned as single in the previous verse shown in Figure 7. The second thing to notice in these two verses is that the “birds” have a language in which Solomon used to command them with. This implicitly indicates that such “birds” have the intelligence that excels to a level of communication language.

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Birds have been mentioned in several other verses in Quran as shown in Figure 10, 11 and 12 respectively. The common thing between these verses is that they start with the interrogative phrase “Do they not see”, addressing mankind. The concept of “believing” is clear in the verse 79 of chapter 16 Al-Nahal given in Figure 10, which is associated with mentioning “the birds”.

FIGURE 7. Verse 38 of Chapter 6, Al-Ana’am.

FIGURE 8. Verse 16 of Chapter 27, Al-Namil.

FIGURE 9. Verse 17 of Chapter 27, Al-Namil.

FIGURE 10. Verse 79 of Chapter 16, Al-Nahal.

FIGURE 11. Verse 19 of Chapter 67, Al-Mulk.

FIGURE 12. Verse 41 of Chapter 24, Al-Noor.

Ants, another form of SI, are also mentioned in the Holy Quran. In verse 18 of Chapter 27, Al-Namil, shown in Figure 13, the verse implicitly indicates ants’ communication ability and the possession of intelligence required to predict and avoid danger. The other thing implied in this verse is the social related behavior of obeying a leader. A form of procedure carried out by these insects to sustain life.

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FIGURE 13. Verse 18 of Chapter 27, Al-Namil. Because the original script of the Holy Quran is Arabic, it is worth to mention that teaching Arabic as foreign language, to non-native speakers, comprises of many difficulties and obstacles encountered in its written form, sentence structure and grammatical patterns [12]. Having an accurately translated version of the Noble Quran is not an easy task as the ordering of phrases, domains, structure and also the chronological order of different verses imposes a challenge [11]. The Quranic verses that are mentioned in this work, and to the best of the authors’ knowledge, are relevant to the SI methods discussed. The Quran is indeed a comprehensive Holy Book, and due to the authors’ limited knowledge, some other relevant verses have been included in this work. CONCLUSION Many SI-based metaheuristics are now commonly used to solve many optimization problems and have proved to be effective optimization techniques. These metaheuristics are stochastic-based, i.e. probabilistic; the rules of randomness are combined to imitate the process that inspired the algorithm. The “inspiration” that lead to the invention and development of such techniques is based on observations from nature. In this work, the authors’ humble effort showed the relevance between some of those SI-based algorithms and several verses of Noble Quran. By showing such relevance, the paper poses the question of whether the Noble Quran could be used as a source of such “inspiration” for many other metaheuristics not covered in this paper, including probably the yet to be invented ones. Based on the justifications and arguments presented, the authors believe that the Noble Quran could be such source of inspiration.

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ACKNOWLEDGEMENTS The authors would like to thank Assistant Prof. Dr. Mohammed Said Abual-Rub of Computer Science Department at Imam Muhammad bin Saud Islamic University in Saudi Arabia for his assistance in evaluating and proofreading this article to make sure that none of its contents violates Islamic religion in any way. REFERENCES [1]

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Data-Level Parallel Approaches for the H.264 Coding: A Review Mohammed Faiz Aboalmaaly1, Rosni Abdullah2, and Ali Kattan3 National Advanced IPv6 Centre, Universiti Sains Malaysia,Minden, 11800, Penang, Malaysia School of Computer Sciences, Universiti Sains Malaysia, Minden, 11800, Penang, Malaysia IT Department, Ishik University, 100 Meter St., Erbil, Iraq 1 [email protected], 2 [email protected], [email protected]

ABSTRACT In order to enable easier transmission and storage of videos, video-coding techniques are used as data compression process that is intended to reduce the size of raw video without sacrificing the visual quality of video. The H.264 is relatively one of the recent video compression standards, which has proved to outperform former standards in terms of compression efficiency. However, it’s associated with mush higher computational complexity. Several software-based as well as hardwarebased approaches have been suggested to tackle this problem by using several flavours of data-level parallel approaches. In this paper, these approaches are presented and compared. The suitability of one particular approach is determined based to the architecture used. Keywords: video coding, video compression, distributed shared memory architectures, encoding latency, parallel scalability, GPU. 1. INTRODUCTION Video compression is a process intending at reducing the size of raw video without sacrificing the visual quality of video in order to enable easier transmission and storage of videos [1]. Video compression is a process that requires the existence of two complementary systems; the encoder and the decoder. Prediction, transformation and quantization, and entropy coding are the common techniques in video compression algorithms. The encoder system would the processes above, while the decoder system involves the same processes but in reverse order [2]. The H.264 is relatively one of the recent video compression standards, which has proved to outperform former standards in terms of compression efficiency. However, What makes the H.264 more resource-intensive when compared to previous video compression solutions is the added new features that are intended to further increase the compression efficiency while keeping the visual quality saturated [3]. In a comparison with former standards, the introduction of the new features has noticeably improved the compression efficiency.. Consequently, the computational complexity of the H.264 video coding standard has increased by a factor of ten for the encoder and about 2 to 4 times better for the decoder side [4] It has been proved that the data-level parallelization has outperformed the tasklevel parallelization [5] due to the several kinds of dependencies among the coding components of the H.264 video coding standards. This paper presents reviews some of the attempts aimed at lessening the complexity of the H.264 vide coding standard based on data-level parallelism utilizing different parallel architectures. The rest of this paper is arranged as follows: Section 2 gives the necessary background covering some basic terminology; section 3 covers the different parallel granularities of the H.264 and finally the conclusions are given in the last section. 221

Bio-inspired metaheuristic algorithms and the Quran

Bio-inspired metaheuristic algorithms and the Quran

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