Research on the Construction of Underwater Platform Combat Deduction System Based on Service-Oriented and Multi-Agent Technology

Object-oriented intelligent modeling, model management, etc. are difficult problems in the designing and development of underwater platform combat deduction system. The command and control description model based on OODA loop depicted the business process of underwater platform combat deduction using service-oriented and agent modeling technology and established an underwater platforms deduction system architecture, effectively solving the problem of intelligence, reusing, and extensibility in combat deduction modeling. The chapter has reference value in the designing and development of underwater platforms deduction systems.

Interval Type 2 Fuzzy Fireworks Algorithm for Clustering

This chapter presents Interval Type 2 Fuzzy Fireworks Algorithm for clustering (IT2FWAC). It is an optimization method for finding the optimal number of clusters based on the centroid features which uses the Fireworks Algorithm (FWA), but with a dynamic adjustment of parameters using an Interval Type 2 Fuzzy Inference System (IT2FIS). Three variations of the IT2FWAC are proposed to find the optimal number of clusters for different datasets: IT2FWAC -I, IT2FWAC -II, and IT2FWAC –III. They are explained in detail.

Explosion Operation of Fireworks Algorithm

This chapter briefly reviews the basic explosion mechanism used in the fireworks algorithm (FWA) and comprehensively investigates relevant research on explosion operations. Since the explosion mechanism is one of the most core operations directly affecting the performance of FWA, the authors focus on analyzing the FWA explosion operation and highlighting two novel explosion strategies: a multi-layer explosion strategy and a scouting explosion strategy. The multi-layer explosion strategy allows an individual firework to perform multiple explosions instead of the single explosion used in the original FWA, where each round of explosion can be regarded as a layer; the scouting explosion strategy controls an individual firework to generate spark individuals one by one instead of generating all spark individuals within the explosion amplitude at once. The authors then introduce several other effective strategies to further improve the performance of FWA by full using the information generated by the explosion operation. Finally, the authors list some open topics for discussion.

Increasing Energy Efficiency by Optimizing the Electrical Infrastructure of a Railway Line Using Fireworks Algorithm

This chapter proposes the application of a discrete version of the Fireworks Algorithm (FWA) and a novel PSO-FWA hybrid algorithm to optimize the energy efficiency of a metro railway line. This optimization consists in determining the optimal configuration of the Energy Storage Systems (ESSs) to install in a railway line, including their number, location, and power (kW). The installation of the ESSs will improve the energy efficiency of the system by incrementing the use of the regenerated energy produced by the trains in the braking phases, as the ESSs will store the excess of regenerated energy and return it to the system when necessary. The results for this complex optimization problem produced by the two algorithms are excellent and authors prove that the novel PSO-FWA algorithm proposed in this chapter outperforms the standard FWA.

Development and Performance Analysis of Fireworks Algorithm-Trained Artificial Neural Network (FWANN)

Financial time series are highly nonlinear and their movement is quite unpredictable. Artificial neural networks (ANN) have ample applications in financial forecasting. Performance of ANN models mainly depends upon its training. Though gradient descent-based methods are common for ANN training, they have several limitations. Fireworks algorithm (FWA) is a recently developed metaheuristic inspired from the phenomenon of fireworks explosion at night, which poses characteristics such as faster convergence, parallelism, and finding the global optima. This chapter intends to develop a hybrid model comprising FWA and ANN (FWANN) used to forecast closing prices series, exchange series, and crude oil prices time series. The appropriateness of FWANN is compared with models such as PSO-based ANN, GA-based ANN, DE-based ANN, and MLP model trained similarly. Four performance metrics, MAPE, NMSE, ARV, and R2, are considered as the barometer for evaluation. Performance analysis is carried out to show the suitability and superiority of FWANN.

Last-Position Elimination-Based Fireworks Algorithm for Function Optimization

Fireworks algorithm (FWA) searches the global optimum by the cooperation between the firework with the best fitness named as core firework (CF) and the other non-CFs. Loser-out tournament-based fireworks algorithm (LoTFWA) uses competition as a new manner of interaction. If the fitness of a firework cannot catch up with the best one, it is considered a loser and will be reinitialized. However, its independent selection operator may prevent non-CFs from aggregating to CF in the late search phase if they fall into different local optima. This chapter proposes a last-position, elimination-based fireworks algorithm which allocates more fireworks in the initial process to search. Then for every fixed number of generations, the firework with the worst fitness is eliminated and its sparks is reallocated to other fireworks. In the final stage of search, only CF survives with all the budget of sparks and thus the aggregation of non-CFs to CF is ensured. Experimental results performed show that the proposed algorithm significantly outperforms most of the state-of-the-art FWA variants.

A Survey on the Applications of Swarm Intelligence to Software Verification

Applying swarm intelligence techniques to software engineering problems has appealed to both researchers and practitioners in the software engineering community. This chapter describes issues and challenges of its application to formal verification, which is one of the core research fields in software engineering. Formal verification, which explores how to effectively verify software products by using mathematical technique, often suffers from two open problems. One is the so-called state explosion problem that verification tools need too many computational resources to make verification feasible. The other problem is that the results of verification have often too much complexity for users to understand. While a number of research projects have addressed these problems in the context of traditional formal verification, recent researches demonstrate that Swarm Intelligence is a promising tool to tackle the problems. This chapter presents how Swarm Intelligence can be applied to formal verification, and surveys the state-of-the-art techniques.

Innovative Aspects of Virtual Reality and Kinetic Sensors for Significant Improvement Using Fireworks Algorithm in a Wii Game of a Collaborative Sport

A new report on childhood obesity is published every so often. The bad habits of food and the increasingly sedentary life of children in a border society has caused an alarming increase in the cases of children who are overweight or obese. Formerly, it seemed to be a problem of countries with unhealthy eating habits, such as the United States or Mexico in Latin America, where junk food is part of the diet in childhood. However, obesity is a problem that we already have around the corner and that is not so difficult to fight in children. In the present research the development of an application that reduces the problem of the lack of movement in the childhood of a smart city is considered a future problem which it is the main contribution, coupled with achieving an innovative way of looking for an Olympic sport without the complexity of physically moving to a space with high maintenance costs and considering the adverse weather conditions.

A Fireworks-Based Approach for Efficient Packet Filtering in Firewall

Information protection in computers is gaining a lot of importance in real world applications. To secure the private networks of businesses and institutions, a firewall is installed in a specially designated computer separate from the rest of the network so that no incoming packet can directly get into the private network. The system monitors and blocks the requests from illegal networks. The existing methods of packet filtering algorithms suffer from drawbacks in terms of search space and storage. To overcome the drawbacks, a Fireworks-based approach of packet filtering is proposed in this chapter. Termed Fireworks-based Packet Filtering (FWPF) algorithm, the sparks generated by the fireworks makes a decision about the rule position in the firewall ruleset matching with the incoming packet. The advantage of FWPF is that it reduces the search space when compared to the existing packet filtering algorithms.

Learning From Class Imbalance

Learning a classifier from imbalanced data is one of the most challenging research problems. Data imbalance occurs when the number of instances belonging to one class is much less than the number of instances belonging to the other class. A standard classifier is biased towards the majority class and therefore misclassifies the minority class instances. Minority class instances may be regarded as rare events or unusual patterns that could potentially have a negative impact on the society. Therefore, detection of such events is considered significant. This chapter proposes a FireWorks-based Hybrid ReSampling (FWHRS) algorithm to resample imbalance data. It is used with Weighted Pattern Matching based classifier (PMC+) for classification. FWHRS-PMC+ was evaluated on 44 imbalanced binary datasets. Experiments reveal FWHRS-PMC+ is effective in classification of imbalanced data. Empirical results were validated using non-parametric statistical tests.