Cuttlefish algorithm

Nowadays, with the rapid prevalence of networked machines and Internet technologies, intrusion detection systems are increasingly in demand. Consequently, numerous illicit activities by external and internal attackers need to be detected. Thus, earlier detection of such activities is necessary for protecting data and information. In this paper, we investigated the use of the Cuttlefish optimization algorithm as a new rule generation method for the classification task to deal with the intrusion detection problem. The effectiveness of the proposed method was tested using KDD Cup 99 dataset based on different evaluation methods. The obtained results were also compared with the results obtained by some classical well-known algorithms namely Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighborhood (K-NN). Our experimental results showed that the proposed method demonstrates a good classification performance and provides significantly preferable results when compared with the performance of other traditional algorithms. The proposed method produced 93.9%, 92.2%, and 94.7% in terms of precision, recall, and area under curve, respectively.

Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combination approach based on the ID3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an IDS. The BA is used to generate a subset of features, and the ID3 algorithm is used as a classifier. The proposed model is applied on KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed ID3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.

This paper presents a new meta-heuristic bio-inspired<br> optimization algorithm which is called Cuttlefish Algorithm (CFA).<br> The algorithm mimics the mechanism of color changing behavior of<br> the cuttlefish to solve numerical global optimization problems. The<br> colors and patterns of the cuttlefish are produced by reflected light<br> from three different layers of cells. The proposed algorithm considers<br> mainly two processes: reflection and visibility. Reflection process<br> simulates light reflection mechanism used by these layers, while<br> visibility process simulates visibility of matching patterns of the<br> cuttlefish. To show the effectiveness of the algorithm, it is tested with<br> some other popular bio-inspired optimization algorithms such as<br> Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and<br> Bees Algorithm (BA) that have been previously proposed in the<br> literatu...

Abstract. Several biology inspired optimization algorithms such as Genetic Algorithms, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have previously been proposed by researchers. Recent approaches in numerical optimization have shifted to ...

The allocation of security personnel to duty posts in a university may affect the efficiency of the security personnel and the overall efficiency of the security unit. This may have serious implications for the security of individuals and the organization. The manual mode, which is a conventional method adopted in posting security personnel in most universities focuses more on physical ability but less on emotional intelligence. Previous researches have shown that security job is emotionally demanding. Emotional intelligence may play a key role in the way an individual reacts to and manages emotional information of self and others. This study therefore introduces a system of posting security personnel that takes emotional intelligence as a key determinant factor in the posting of security personnel. Specifically, this study introduces an artificial intelligence heuristic method of allocation (genetic algorithm) for the scheduling of security officers with respect to their emotional ...

Scheduling lecture courses in any educational institution is a very complex problem, which involves many entities in preparing the lecture schedule. The Genetic Algorithm and Tabu Search can assist in the preparation of lecture schedules, specifically the lecture schedule at the Universitas Teknokrat Indonesia. Universitas Teknokrat Indonesia has 3 faculties, which are the Faculty of Engineering and Computer Science, Faculty of Literature and Education, and Faculty of Economics and Business. The case study of this research focuses on Information Systems and Informatics Engineering departments. In this research, the author aim was to improve the previous researches in scheduling problem by combining two algorithms in scheduling lecture by reducing the domain of search and determining tabu values according to the case in Universitas Teknokrat Indonesia. The experiment has been carried out using combination data of 3.753.552 on which the total slot in 1 population as 303 data. The resu...

This paper presents a new meta-heuristic bio-inspired<br> optimization algorithm which is called Cuttlefish Algorithm (CFA).<br> The algorithm mimics the mechanism of color changing behavior of<br> the cuttlefish to solve numerical global optimization problems. The<br> colors and patterns of the cuttlefish are produced by reflected light<br> from three different layers of cells. The proposed algorithm considers<br> mainly two processes: reflection and visibility. Reflection process<br> simulates light reflection mechanism used by these layers, while<br> visibility process simulates visibility of matching patterns of the<br> cuttlefish. To show the effectiveness of the algorithm, it is tested with<br> some other popular bio-inspired optimization algorithms such as<br> Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and<br> Bees Algorithm (BA) that have been previously proposed in the<br> literatu...

Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combination approach based on the ID3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an IDS. The BA is used to generate a subset of features, and the ID3 algorithm is used as a classifier. The proposed model is applied on KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed ID3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.

Different Distributed Intrusion Detection Systems (DIDS) based on mobile agents have been proposed in recent years to protect computer systems from intruders. Since intrusion detection systems deal with a large amount of data, keeping the best quality of features is an important task in these systems. In this paper, a novel DIDS based on the combination of Cuttlefish Optimization Algorithm (CFA) and Decision Tree (DT) is proposed. The proposed system uses an agent called Rule and Feature Generator Agent (RFGA) to generate a subset of features with corresponding rules. RFGA agent uses CFA to search for optimal subset of features, while DT is used as a measurement on the selected features. The proposed model is tested on the KDD Cup 99 dataset. The obtained results show that the proposed system gives a better performance even with a small subset of 5 features when compared with using all 41 features.

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.

This paper provides a novel feature selection method based on Cuttle Fish Augmentation (CFA) technique, Intrusion Detection systems (IDSs) are the platforms which will utilize this CFA. One of the serious task of IDSs is to maintain the quality features that represents the entire data, since a large volume of information is handled by the IDSs. The redundant feature were removed by the IDSs. A search scheme proposed in this paper called as Cuttle Fish Augmentation (CFA) which is used to determine the optimal subset features and decision on the particular features will be detected by Decision Tree (DT) classifier, these are created by the CFA. The suggested CFA model is estimated by the KDD Cup 99 data set. The results shows that, the feature subsets acquired by using the CFA produces increased detection rate and increased accuracy rate with a lower false alarm rate,as compared with different features that has been observed earlier and it is also clear that our proposed system is efficient.

Abstract—Designing of a network which could fulfill most of the requirements is always a challenging task for a researcher. Often this happens either with manual approach or by applying some kind of conventional methods. In both cases results do not have high level ...

This paper presents a new meta-heuristic bio-inspired<br> optimization algorithm which is called Cuttlefish Algorithm (CFA).<br> The algorithm mimics the mechanism of color changing behavior of<br> the cuttlefish to solve numerical global optimization problems. The<br> colors and patterns of the cuttlefish are produced by reflected light<br> from three different layers of cells. The proposed algorithm considers<br> mainly two processes: reflection and visibility. Reflection process<br> simulates light reflection mechanism used by these layers, while<br> visibility process simulates visibility of matching patterns of the<br> cuttlefish. To show the effectiveness of the algorithm, it is tested with<br> some other popular bio-inspired optimization algorithms such as<br> Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and<br> Bees Algorithm (BA) that have been previously proposed in the<br> literatu...

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.

Scheduling lecture courses in any educational institution is a very complex problem, which involves many entities in preparing the lecture schedule. The Genetic Algorithm and Tabu Search can assist in the preparation of lecture schedules, specifically the lecture schedule at the Universitas Teknokrat Indonesia. Universitas Teknokrat Indonesia has 3 faculties, which are the Faculty of Engineering and Computer Science, Faculty of Literature and Education, and Faculty of Economics and Business. The case study of this research focuses on Information Systems and Informatics Engineering departments. In this research, the author aim was to improve the previous researches in scheduling problem by combining two algorithms in scheduling lecture by reducing the domain of search and determining tabu values according to the case in Universitas Teknokrat Indonesia. The experiment has been carried out using combination data of 3.753.552 on which the total slot in 1 population as 303 data. The resu...

The allocation of security personnel to duty posts in a university may affect the efficiency of the security personnel and the overall efficiency of the security unit. This may have serious implications for the security of individuals and the organization. The manual mode, which is a conventional method adopted in posting security personnel in most universities focuses more on physical ability but less on emotional intelligence. Previous researches have shown that security job is emotionally demanding. Emotional intelligence may play a key role in the way an individual reacts to and manages emotional information of self and others. This study therefore introduces a system of posting security personnel that takes emotional intelligence as a key determinant factor in the posting of security personnel. Specifically, this study introduces an artificial intelligence heuristic method of allocation (genetic algorithm) for the scheduling of security officers with respect to their emotional ...

Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combination approach based on the ID3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an IDS. The BA is used to generate a subset of features, and the ID3 algorithm is used as a classifier. The proposed model is applied on KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed ID3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.

Genetic Algorithms have been widely applied to various kinds of optimisation problems. In this work, a Genetic Algorithm is designed to solve the three classic numerical optimisation problems – Rastrigin, Schwefel and Griewank. An experiment to observe the comparative performances of five different crossover functions was conducted. Also, the possible effect of aging out some of the old individuals from the population was hinted at. A parameter set expected to give the optimal performance and a discussion on the design considerations are presented below. 1.0 Background Introduction The field of evolutionary computation is a rapidly growing one. Mitchell [Mit96], in discussing the use of evolution as an inspiration for solving computational problems, observed that, ..the mechanism of evolution seem well suited for some of the most pressing computational problems in many fields. Many computational problems require searching through a huge number of possibilities for solutions. Genetic...

Gunumuzde, aga bagli makinelerin ve Internet teknolojilerinin hizla yayginlasmasiyla, saldiri tespit sistemleri giderek daha fazla talep gormektedir. Buna bagli olarak, dis ve ic saldirganlarin cok sayida yasadisi faaliyetinin tespit edilmesi gerekmektedir. Bu nedenle, veri ve bilgilerin korunmasi icin bu tur yasadisi faaliyetlerin erken tespiti gerekli ve onemlidir. Bu makalede, veri madenciliginde saldiri tespit problemiyle basa cikmak amaciyla Murekkepbaligi Optimizasyon Algoritmasinin yeni bir kural olusturma yontemi olarak kullanimi arastirilmistir. Onerilen yontemin etkinligi, farkli degerlendirme yontemlerine dayali olarak KDD Cup 99 veri seti kullanilarak test edilmistir. Ayrica, elde edilen sonuclar Karar Agaci, Naive Bayes, Destek Vektor Makinesi ve K-En Yakin Komsu gibi bazi klasik iyi bilinen algoritmalar ile alinan sonuclarla karsilastirilmistir. Deneysel sonuclarimiz, onerilen yontemin iyi bir siniflandirma performansi sergiledigini ve diger geleneksel algoritmalarin p...

The allocation of security personnel to duty posts in a university may affect the efficiency of the security personnel and the overall efficiency of the security unit. This may have serious implications for the security of individuals and the organization. The manual mode, which is a conventional method adopted in posting security personnel in most universities focuses more on physical ability but less on emotional intelligence. Previous researches have shown that security job is emotionally demanding. Emotional intelligence may play a key role in the way an individual reacts to and manages emotional information of self and others. This study therefore introduces a system of posting security personnel that takes emotional intelligence as a key determinant factor in the posting of security personnel. Specifically, this study introduces an artificial intelligence heuristic method of allocation (genetic algorithm) for the scheduling of security officers with respect to their emotional ...

The allocation of security personnel to duty posts in a university may affect the efficiency of the security personnel and the overall efficiency of the security unit. This may have serious implications for the security of individuals and the organization. The manual mode, which is a conventional method adopted in posting security personnel in most universities focuses more on physical ability but less on emotional intelligence. Previous researches have shown that security job is emotionally demanding. Emotional intelligence may play a key role in the way an individual reacts to and manages emotional information of self and others. This study therefore introduces a system of posting security personnel that takes emotional intelligence as a key determinant factor in the posting of security personnel. Specifically, this study introduces an artificial intelligence heuristic method of allocation (genetic algorithm) for the scheduling of security officers with respect to their emotional ...

Gunumuzde, aga bagli makinelerin ve Internet teknolojilerinin hizla yayginlasmasiyla, saldiri tespit sistemleri giderek daha fazla talep gormektedir. Buna bagli olarak, dis ve ic saldirganlarin cok sayida yasadisi faaliyetinin tespit edilmesi gerekmektedir. Bu nedenle, veri ve bilgilerin korunmasi icin bu tur yasadisi faaliyetlerin erken tespiti gerekli ve onemlidir. Bu makalede, veri madenciliginde saldiri tespit problemiyle basa cikmak amaciyla Murekkepbaligi Optimizasyon Algoritmasinin yeni bir kural olusturma yontemi olarak kullanimi arastirilmistir. Onerilen yontemin etkinligi, farkli degerlendirme yontemlerine dayali olarak KDD Cup 99 veri seti kullanilarak test edilmistir. Ayrica, elde edilen sonuclar Karar Agaci, Naive Bayes, Destek Vektor Makinesi ve K-En Yakin Komsu gibi bazi klasik iyi bilinen algoritmalar ile alinan sonuclarla karsilastirilmistir. Deneysel sonuclarimiz, onerilen yontemin iyi bir siniflandirma performansi sergiledigini ve diger geleneksel algoritmalarin p...

Gunumuzde, aga bagli makinelerin ve Internet teknolojilerinin hizla yayginlasmasiyla, saldiri tespit sistemleri giderek daha fazla talep gormektedir. Buna bagli olarak, dis ve ic saldirganlarin cok sayida yasadisi faaliyetinin tespit edilmesi gerekmektedir. Bu nedenle, veri ve bilgilerin korunmasi icin bu tur yasadisi faaliyetlerin erken tespiti gerekli ve onemlidir. Bu makalede, veri madenciliginde saldiri tespit problemiyle basa cikmak amaciyla Murekkepbaligi Optimizasyon Algoritmasinin yeni bir kural olusturma yontemi olarak kullanimi arastirilmistir. Onerilen yontemin etkinligi, farkli degerlendirme yontemlerine dayali olarak KDD Cup 99 veri seti kullanilarak test edilmistir. Ayrica, elde edilen sonuclar Karar Agaci, Naive Bayes, Destek Vektor Makinesi ve K-En Yakin Komsu gibi bazi klasik iyi bilinen algoritmalar ile alinan sonuclarla karsilastirilmistir. Deneysel sonuclarimiz, onerilen yontemin iyi bir siniflandirma performansi sergiledigini ve diger geleneksel algoritmalarin p...

Gunumuzde, aga bagli makinelerin ve Internet teknolojilerinin hizla yayginlasmasiyla, saldiri tespit sistemleri giderek daha fazla talep gormektedir. Buna bagli olarak, dis ve ic saldirganlarin cok sayida yasadisi faaliyetinin tespit edilmesi gerekmektedir. Bu nedenle, veri ve bilgilerin korunmasi icin bu tur yasadisi faaliyetlerin erken tespiti gerekli ve onemlidir. Bu makalede, veri madenciliginde saldiri tespit problemiyle basa cikmak amaciyla Murekkepbaligi Optimizasyon Algoritmasinin yeni bir kural olusturma yontemi olarak kullanimi arastirilmistir. Onerilen yontemin etkinligi, farkli degerlendirme yontemlerine dayali olarak KDD Cup 99 veri seti kullanilarak test edilmistir. Ayrica, elde edilen sonuclar Karar Agaci, Naive Bayes, Destek Vektor Makinesi ve K-En Yakin Komsu gibi bazi klasik iyi bilinen algoritmalar ile alinan sonuclarla karsilastirilmistir. Deneysel sonuclarimiz, onerilen yontemin iyi bir siniflandirma performansi sergiledigini ve diger geleneksel algoritmalarin p...

This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. The proposed algorithm considers mainly two processes: reflection and visibility. Reflection process simulates light reflection mechanism used by these layers, while visibility process simulates visibility of matching patterns of the cuttlefish. To show the effectiveness of the algorithm, it is tested with some other popular bio-inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior when compared with these algorithms.

In this paper, a new meta-heuristic bio-inspired optimization algorithm, called Cuttlefish Algorithm (CFA) is presented. The algorithm mimics the mechanism of color changing behavior used by the cuttlefish to solve numerical global optimization problems. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors. The proposed algorithm considers two main processes: reflection and visibility. Reflection process is proposed to simulate the light reflection mechanism used by these three layers, while the visibility is proposed to simulate the visibility of matching pattern used by the cuttlefish. These two processes are used as a search strategy to find the global optimal solution. Efficiency of this algorithm is also tested with some other popular biology inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior to other algorithms.

In this paper, a new meta-heuristic bio-inspired optimization algorithm, called Cuttlefish Algorithm (CFA) is presented. The algorithm mimics the mechanism of color changing behavior used by the cuttlefish to solve numerical global optimization problems. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors. The proposed algorithm considers two main processes: reflection and visibility. Reflection process is proposed to simulate the light reflection mechanism used by these three layers, while the visibility is proposed to simulate the visibility of matching pattern used by the cuttlefish. These two processes are used as a search strategy to find the global optimal solution. Efficiency of this algorithm is also tested with some other popular biology inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior to other algorithms.

— In this paper, a new meta-heuristic bio-inspired optimization algorithm, called Cuttlefish Algorithm (CFA) is presented. The algorithm mimics the mechanism of color changing behavior used by the cuttlefish to solve numerical global optimization problems. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors. The proposed algorithm considers two main processes: reflection and visibility. Reflection process is proposed to simulate the light reflection mechanism used by these three layers, while the visibility is proposed to simulate the visibility of matching pattern used by the cuttlefish. These two processes are used as a search strategy to find the global optimal solution. Efficiency of this algorithm is also tested with some other popular biology inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior to other algorithms.

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.

—This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. The proposed algorithm considers mainly two processes: reflection and visibility. Reflection process simulates light reflection mechanism used by these layers, while visibility process simulates visibility of matching patterns of the cuttlefish. To show the effectiveness of the algorithm, it is tested with some other popular bio-inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior when compared with these algorithms.

This paper presented a comparative study of the application of two Swarm Intelligence algorithms: Particle Swarm Optimization and Firefly Algorithm in automatic camera calibration problem. The fitness function used in the camera calibration problem is based on the Kruppa's equation. A case study from a dataset provided by Le2i Universite de Bourgoune is taken for benchmarking the performance of both algorithms. The result is compared with previous literatures. Result obtained from these algorithms indicates there is potential for further improvement.