Coral Reefs Optimization Algorithm Research Articles

CROA-based feature selection with BERT model for detecting the offensive speech in Twitter data

<p>Online hate speech has flourished on social networking sites due to the widespread availability of mobile computers and other Web knowledge. Extensive research has shown that online exposure to hate speech has real-world effects on marginalized communities. Research into methods of automatically identifying hate speech has garnered significant attention. Hate speech can affect any demographic, while some populations are more vulnerable than others. Relying solely on progressive learning is insufficient for achieving the goal of automatic hate speech identification. It need access to large amounts of labelled data to train a model. Inaccurate statistics on hate speech and preconceived notions have been the biggest obstacles in the field of hate speech research for a long time. This research provides a novel strategy for meeting these needs by combining a transfer-learning attitude-based BERT (Bidirectional Encoder Representations from Transformers) with a coral reef optimization-based approach (CROA). A feature selection (FC) optimization strategy for coral reefs, a coral reefs optimization method mimics coral behaviours for reef location and development. We might think of each potential answer to the problem as a coral trying to establish itself in the reefs. The results are refined at each stage by applying specialized operators from the coral reefs optimization algorithm. When everything is said and done, the optimal solution is chosen. We also use a cutting-edge fine-tuning method based on transfer learning to assess BERT’s ability to recognize hostile contexts in social media communications. The paper evaluates the proposed approach using Twitter datasets tagged for racist, sexist, homophobic, or otherwise offensive content. The numbers show that our strategy achieves 5%–10% higher precision and recall compared to other approaches.</p>

Multiobjective Edge Server Placement in Mobile-Edge Computing Using a Combination of Multiagent Deep Q-Network and Coral Reefs Optimization

The growth of telecommunication technologies, especially 5G, the growing popularity of smart mobile devices, the emergence of smart cities and Internet of Things (IoT), and the easy use of these equipments have led the cloud users to utilize their various services. Real-time applications and the use of big data have caused cloud service providers (CSPs) to move their servers to the edge of the network and in the vicinity of users to maintain the quality of their services. For this purpose, the concept of mobile-edge computing (MEC) was formed. Applications often have heavy computing complexity on mobile devices or require a lot of data to process. Moreover, in order to save energy consumption of the batteries of this equipment, offloading them on the network resources can transfer the computational complexity from the users’ equipment to the network resources. The resource placement (RP) is one of the major challenges in this area. Improper resource topology upsets their load balancing and increases access latency. In the proposed method of this article, the cellular mobile network is divided into smaller areas and using the coral reefs optimization (CRO) algorithm, the optimal placement of resources in each of these areas will be locally performed. The deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DQN) and Markov game (MG) are used to optimize global RP to reduce global latency and to improve resource load balancing as its two objectives. The results of the experiments show that the proposed method has significantly improved its objectives and server’s energy efficiency, compared to some similar works in this area.

A centroid opposition-based coral reefs algorithm for solving an automated guided vehicle routing problem with a recharging constraint

Automated guided vehicles (AGVs) have been utilized extensively in material handling systems. In this paper, a synchronization problem of task scheduling and AGV routing is studied on a shop floor with reconfigurable machine tools. The objective is to minimize the maximum completion time (i.e., makespan) of all products. Studying scheduling and routing problems simultaneously, we propose a centroid opposition-based coral reefs optimization (COCRO) algorithm combined with a heuristic. The developed heuristic uses the Dijkstra algorithm to extract collision-free paths. To test the performance of the proposed algorithm, its results are compared to the results of the algorithm used in the literature recently. The results indicate the superiority of the COCRO algorithm. Finally, the performance of the system under different circumstances is analyzed. Based on the results, it can be claimed that reconfigurable machines reduce production time by up to 35%. Furthermore, the capacity of AGVs has a more significant role than the number of AGVs in the production time. Also, comparing four widely used types of batteries (i.e., sealed GEL, AGM pure-lead, Lithium, and flooded lead-acid), the result confirms the superiority of Lithium batteries.

A versatile multi-method ensemble for wind farm layout optimization

In this paper, the performance of a multi-method ensemble for optimization in a turbine layout problem for wind farms is discussed. Specifically, the Coral Reefs Optimization algorithm with Substrate Layer (CRO-SL) is evaluated. The CRO-SL is an evolutionary-type meta-heuristic, able to combine different search procedures or methods within a single population, leading to a multi-method ensemble. The CRO-SL has been adapted to the wind turbines layout problem by including a discrete encoding, based on sparse matrices (though it can also work over continuous encodings) and a convex geometric procedure to speed-up the wake effect calculation for each turbine. The performance of the proposed CRO-SL algorithm in the turbine layout problem is evaluated by means of different experiments, considering simulated and real data from a wind farm in Spain. The performance of the CRO-SL is also evaluated in a different wind turbine layout problem from a NREL-IEA case competition. The contribution of each substrate of the CRO-SL algorithm (search procedure) is discussed in terms of the global search ability of the algorithm in the different problems considered. The global performance of the proposed approach has been finally compared to that of existing approaches in the literature, obtaining excellent results in all scenarios considered.

Novel Real Valued Improved Coral-Reef Optimization Algorithm for Optimal Integration of Classified Distributed Generators

In recent times, Distributed Generation (DG) penetration, especially, through Renewable Energy Sources (RES) has been growing immensely due to marginal carbon footprints. Furthermore, they make system more reliable by minimizing the voltage deviation of the distribution network. Nevertheless, to exploit the DGs in the best manner, they need to be sited and rated optimally. Diverse solutions exist for the problem mainly classified as: analytical approaches, classical non-linear optimization algorithms and meta-heuristic methods. With the objective of minimizing power loss, this manuscript proposes a novel hybrid meta-heuristics approach: Particle Swarm Optimization-Coral Reef Optimization (PSO-CRO) for identifying an optimum positioning and rating of Type-1, Type-2 &#x0026; Type-3 DGs (at 0.82 optimal power factor) in IEEE 33, 69 &#x0026; 118 bus Radial Distributed System (RDS). Furthermore, the results from the proposed hypothesis are compared with its prevalent peers, namely, PSO, CRO, Gravitational Search Algorithm (GSA), PSO-GSA and PSO-Grey Wolf Optimization (PSO-GWO) etc. The results of the proposed algorithm are also compared with the results of GAMS/CONOPT commercial solver. The simulation results prove the robustness, higher efficacy and faster convergence of the proposed method when applied to larger distribution systems.

Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches

This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bending stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch’s geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specifically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Differential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.

Optimal Location and Sizing of Energy Storage Systems in DC-Electrified Railway Lines Using a Coral Reefs Optimization Algorithm with Substrate Layers

This paper deals with the problem of finding the optimal location and sizing of Energy Storage Systems in DC-electrified railway lines. These devices increment the use of the regenerated energy produced by the trains in the braking phases, as they store the energy to later provide to the catenary the excess of regenerated energy, that otherwise would be lost in the rheostats. However, these infrastructures require a high initial investment that, in some cases, may question their profitability. We propose a multi-method ensemble meta-heuristic to obtain the optimal solution to the problem, with a high level of accuracy. Specifically, the Coral Reefs Optimization with Substrate Layers (CRO-SL) is proposed, an evolutionary-type approach able to run different search procedures within the same population. We will evaluate the performance of the CRO-SL in the problem, and we will show that it performs better than the best known existing meta-heuristics for this problem.

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function.

Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem. This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.

The explosion operation of fireworks algorithm boosts the coral reef optimization for multimodal medical image registration

Medical image registration is becoming increasingly important in diagnosis and treatment planning, as it enables to align and integrate different images having a shared content, which obtained under different conditions, into a single representation. The most challenging issues of registration is finding a optimal transformation which settled by optimization methods. With this work, we present a Fireworks Algorithm (FWA) boosts the Coral Reefs Optimization Algorithm (CRO) for medical image registration (FWBCR). Firstly, the Coral Reefs Optimization is easily trapping in local optimum, we enhance the exploration ability of the CRO by integrated with a explosion operator of the FWA. At the Broadcast Spawning stage of CRO, a large fraction of corals are applying the crossover operator which has a good exploitation ability and a small fraction of corals with good solutions are applying the explosion operator which has a good exploration ability. Therefore, the algorithm we proposed is well solved the trades off between exploitation and exploration. Secondly, the explosion amplitude affects the search ability. A larger explosion amplitude benefit for the exploration in the early stages of the algorithm. Contrary, a smaller explosion amplitude benefit for the exploitation in the late stages of the algorithm. To deal with these problems, we design an adaptive explosion amplitude which depends on the fitness of the best solution and the worst solution. Thirdly, the phenomenon of the explosion operator is to generate lots of sparks. It is hard to select a small number of sparks with less computation effort while to guarantee the diversity of the swarm for next generation from a large number of sparks. To overcome this difficulty, we exploring the use of the clustering algorithm on the sparks then get some cluster centers as the new solutions. In this way, the diversity of the swarm can be ensured and the computation effort can be greatly reduced through reducing the number of evaluations. Finally, inspired by the Differential Evolution Algorithm, we construct a differential migration vector(DMV) with the most promising right direction and adaptive length, and to generate elite solutions by adding the DMV to the position of the spark centers. The FWBCR has been tested in numerous experiments on benchmark datasets include six kind of different modality images, from up to eighteen different patients, which can make up 54 multimodal registration scenarios. For registration precision, FWBCR obtained best in 19 scenarios, BBO-EL obtained best in 22 scenarios, while CRO-SL obtained best in 13 scenarios, which demonstrated that FWBCR outperformed the CRO variants like CRO-SL in most cases and as good as the state-of-the-art in the registration field. In addition, compared with BBO-EL, FWBCR achieve fast convergence rate and increase computing performance by 30%.

A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation

The continuous growing demand for water, prolonged periods of drought, and climatic uncertainties attributed mainly to climate change mean surface water reservoirs more than ever need to be managed efficiently. Several optimization algorithms have been developed to optimize multi-reservoir systems operation, mostly during severe dry/wet seasons, to mitigate extreme-events consequences. Yet, convergence speed, presence of local optimums, and calculation-cost efficiency are challenging while looking for the global optimum. In this paper, the problem of finding an efficient optimal operation policy in multi-reservoir systems is discussed. The complexity of the long-term operating rules and the reservoirs' upstream and downstream joint-demands projected in recursive constraints make this problem formidable. The original Coral Reefs Optimization (CRO) algorithm, which is a meta-heuristic evolutionary algorithm, and two modified versions have been used to solve this problem. Proposed modifications reduce the calculation cost by narrowing the search space called a constrained-CCRO and adjusting reproduction operators with a reinforcement learning approach, namely the Q-Learning method (i.e., the CCRO-QL algorithm). The modified versions search for the optimum solution in the feasible region instead of the entire problem domain. The models’ performance has been evaluated by solving five mathematical benchmark problems and a well-known continuous four-reservoir system (CFr) problem. Obtained results have been compared with those in the literature and the global optimum, which Linear Programming (LP) achieves. The CCRO-QL is shown to be very calculation-cost-effective in locating the global optimum or near-optimal solutions and efficient in terms of convergence, accuracy, and robustness.

Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments

Resource management is the process of task scheduling and resource provisioning to provide requirements of cloud users. Since cloud resources are often heterogeneous, task scheduling and resource provisioning are major challenges in this area. Various methods have been introduced to improve resource utilization and thus increase the efficiency of cloud computing. Existing methods can be divided into several categories, including mathematical and statistical methods, heuristic- and meta-heuristic-based methods, and machine-learning-based methods. Since the resource management problem is NP-complete, several optimization methods have been also exploited in this area. Coral reefs algorithm is an evolutionary method that has showed appropriate convergence and response time for some problems, and thus is used in this paper to combine with reinforcement learning to improve efficiency of resource management in cloud environments. The proposed method of this paper consists of two phases. The initial allocation of resources to ready-to-perform tasks is done using the coral reefs algorithm in the first phase. The tasks are considered as corals and the resources are considered reefs in this method. The second phase utilizes reinforcement learning to avoid falling into the local optima and to make optimal use of resources using a long-term approach. The proposed model of this paper, called MO-CRAML, introduces a new hybrid algorithm for improving utilization and load balancing of cloud resources using the combination of coral reefs optimization algorithm and reinforcement learning. The results of the experiments show that the proposed algorithm has better performance in cloud resource utilization and load balancing in comparison with some other important methods of the literature.

Addressing Unequal Area Facility Layout Problems with the Coral Reef Optimization algorithm with Substrate Layers

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant task in industrial manufacturing, in which the disposition of a number of facilities (or departments) in a manufacturing system must be obtained, under several optimization criteria and different constraints. The UA-FLP is a hard optimization problem, in which traditional optimization techniques do not obtain good results. Thus, it has been successfully tackled with different heuristics and meta-heuristics in the last years. In this work we address the UA-FLP with a multi-method ensemble approach, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL). It is a novel multi-method evolutionary algorithm that encourages the evolution of several searching procedures at the same time over a single population. The CRO-SL has been previously applied to very difficult optimization problems, obtaining excellent performance. In this case, we adapt the CRO-SL to the UA-FLP, by means of increasing the diversity generation within the algorithm, which is helpful to improve the exploration of the searching space, avoiding to fall into local minima. Specifically, we propose to include several reproduction mechanisms (adapted to the UA-FLP) within each substrate of the algorithm, which will highly increase the diversity generation in the CRO-SL. An exhaustive experimental study of the CRO-SL performance in a large number of UA-FLP instances is carried out, including a comparison with the state-of-the-art algorithms for this problem. We will show the ability of the CRO-SL to reach or surpass the best-known solutions in most of the tested UA-FLP cases.

A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an Interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions.

Submerged Arches Optimal Design With a Multi-Method Ensemble Meta-Heuristic Approach

This paper discusses the performance of a multi-method ensemble meta-heuristic for optimization in a problem of submerged arches design. Specifically, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL) is proposed. It is a multi-method evolutionary ensemble which combines different searching structures in a single population. In this case, some novel searching strategies such as the Firefly algorithm and the Water Wave Optimization have been proposed to be included in the CRO-SL algorithm. In addition, some other traditional searching structures such as classical crossovers, Gaussian mutation operators, Harmony search and Differential Evolution search strategies have also been implemented in the algorithm. The proposed CRO-SL has been applied to the optimal design of submerged arches in deep waters, considering several design aspects such as the bending moment of the beam and the airspace of the arch. The evaluation of the submerged arches quality is obtained after the simulation of the arches by means of a finite elements computational software, which has been hybridized with the CRO-SL algorithm in the process of fitness calculation. It will be shown that the CRO-SL is able to obtain excellent solutions to submerged arches design problems. For this, the computational performance of each substrate (different searching strategies) in this optimization problem will be discussed and the results obtained will be compared with those in alternative works for similar experiments.

A Hybrid Coral Reefs Optimization—Variable Neighborhood Search Approach for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant optimization problem related to industrial design, that deals with obtaining the most effective allocation of facilities, that make up the rectangular manufacturing plant layout. The UA-FLP is known to be a hard optimization problem, where meta-heuristic approaches are a good option to obtain competitive solutions. Many of these computational approaches, however, usually fall into local optima, and suffer from lack of diversity in their population, mainly due to the huge search spaces and hard fitness landscapes produced by the traditional representation of UA-FLP. To solve these issues, in this paper we propose a novel hybrid meta-heuristic approach, which combines a Coral Reefs Optimization algorithm (CRO) with a Variable Neighborhood Search (VNS) and a new representation for the problem, called Relaxed Flexible Bay Structure (RFBS), which simplifies the encoding and makes its fitness landscape more affordable. Thus, the use of VNS allows more intensive exploitation of the searching space with an affordable computational cost, as well as the RFBS allows better management of the free space into the plant layout. This combined strategy has been tested over a set of UA-FLP instances of different sizes, which have been previously tackled in the literature with alternative meta-heuristics. The tests results show very good performance in all cases.

A novel Island Model based on Coral Reefs Optimization algorithm for solving the unequal area facility layout problem

This paper proposes a novel approach to address the Unequal Area Facility Layout Problem (UA-FLP), based on the combination of both an Island Model and a Coral Reefs Optimization (CRO) algorithm. Two different versions of this Island Model based on Coral Reefs Optimization Algorithm (IMCRO) are proposed and applied to the UA-FLP. The structure of flexible bays has been selected as effective encoding to represent the facility layouts within the algorithm. The two versions of the proposed approach have been tested in 22 UA-FLP cases, considering small, medium and large size categories. The empirical results obtained are compared with previous state of the art algorithms, in order to show the performance of the IMCRO. From this comparison, it can be extracted that both versions of the proposed IMCRO algorithm show an excellent performance, accurately solving the UA-FLP instances in all the size categories.

Applying the coral reefs optimization algorithm for solving unequal area facility layout problems

Coral Reefs Optimization (CRO) is a recently proposed evolutionary-type algorithm which has shown promising results to tackle many complex optimization problems. This paper discusses the performance of this meta-heuristic in Unequal Area Facility Layout Problems (UA-FLPs). The UA-FLP is an important problem in industrial production, which considers a rectangular region and a set of rectangular facilities. These facilities must be allocated in the plant in the most adequate way satisfying certain constraints. The Flexible Bay Structure has been selected in order to represent solutions for the UA-FLP in the proposed CRO algorithm. In this paper, we detail the implementation of the algorithm and provide the results of different tests in several UA-FLP instances with different size and setting. The obtained results confirm the excellent performance of the proposed algorithm in solving UA-FLPs, improving alternative algorithms devoted to this problem in the literature.

A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments

High response time of analytical queries is one of the most challenging issues of data warehouses. Complicated nature of analytical queries and enormous volume of data are the most important reasons of this high response time. The aim of materialized view selection is to reduce the response time of these analytical queries. For this purpose, the search space is firstly constructed by producing the set of all possible views based on given queries and then, the (semi-) optimal set of materialized views will be selected so that the queries can be answered at the lowest cost using them. Various materialized view selection methods have been proposed in the literature, most of which are randomized methods due to the time-consuming nature of this problem. Randomized view selection methods choose a semi-optimal set of proper views for materialization in an appropriate time using one or a combination of some meta-heuristic(s). In this paper, a novel coral reefs optimization-based method is introduced for materialized view selection in a data warehouse. Coral reefs optimization algorithm is an optimization method that solves problems by simulating the coral behaviors for placement and growth in reefs. In the proposed method, each solution of the problem is considered as a coral, which is always trying to be placed and grow in the reefs. In each step, special operators of the coral reefs optimization algorithm are applied on the solutions. After several steps, better solutions are more likely to survive and grow on the reefs. The best solution is finally chosen as the final solution of the problem. The practical evaluations of the proposed method show that this method offers higher quality solutions than other similar random methods in terms of coverage rate of queries.

A high-performance parallel coral reef optimization for data clustering

As a critical research topic toward the new era of big data, how to develop a high-performance data analytics system has received significant research attention from different disciplines since the 2000s. In the literature, many recent works attempted to develop a high-performance data analytics system to handle the large amount of data (i.e., volume) from different information systems (i.e., variety) that typically will be created very quickly in a short time (i.e., velocity). In particular, several recent studies have shown that metaheuristic algorithms can be applied to many data mining optimization problems to provide a better way to find a high-quality result than traditional deterministic algorithms. A high-performance clustering algorithm for big data analytics system will be presented in this paper. The proposed algorithm is designed based on a new kind of metaheuristic algorithm, coral reef optimization with substrate layers (CRO-SL), to get a better cluster result. To improve the effectiveness and efficiency, the proposed CRO-SL scheme has been applied to a cloud computing platform as well to reduce the response time of a data analytics system. The simulation results show that the proposed algorithm is able to provide a better clustering result than the other clustering algorithms compared in this research, including k-means, genetic k-means algorithm, particle swarm optimization, and simple coral reef optimization algorithm in terms of the sum of squared errors.