Optimal Image Fusion Algorithm using Modified Grey Wolf Optimization amalgamed with Cuckoo Search, Levy Fly and Mantegna Algorithm

Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In this paper, the Grey Wolf Optimization (GWO) algorithm with the help of the Shuffled Frog Leaping Algorithm (SFLA) has been proposed. That helps to find the object and allows doctors to take some action. The optimization algorithm is examined with a demonstrated example in order to simplify its steps. The result of the proposed algorithm is compared with other optimization algorithms. The proposed method's performance was always the best among them.

Speech signal quality augmentation involves the challenge to scale down the noise and to perk up the transparency In this paper the performance of two optimization algorithms is compared for denoising the speech signal. The optimization techniques such as Artificial Bee Colony(ABC), Cuckoo Search (CS)algorithm, are used as adaptive algorithms for generating the parameters of adaptive filtering function required for optimum performance. Both techniques are analyzed for different noises at different levels. Performance of these algorithms is evaluated in terms of SNR. In the analysis it is found that CS algorithm gives 45% better result than ABC.

Maximum power point tracking (MPPT) control is one of the essential requirements in harnessing wind power of wind energy conversion systems (WECS). The more precise the maximum power point (MPP) is determined, the more optimal the WECS is operated. Amongst MPPT algorithms, a hill-climb search (HCS) algorithm is preferred because of its simplicity however it also has few drawbacks such as the difficulty of selecting an appropriate step size, the premature convergence phenomenon and the speed-efficiency trade-off. A cuckoo search (CS) algorithm is proposed in this paper for finding out a MPP of a WECS driven by a doubly-fed induction generator (DFIG) under various wind speeds which mostly overcomes the above disadvantages. Then, the DFIG-WECS is controlled to track the MPP. This ensures that the DFIG-WECS is always operated at MPPs regardless of various wind speeds. It is realized that the proposed CS algorithm is also a population-based algorithm inspired from the breeding behavior of cuckoos but it is quite simple, powerful and especially requires less parameters to define optimal solutions than a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. Though numerical results, the CS algorithm shows its effectiveness in finding out MPPs in the DFIG-WECS. Furthermore, the obtained results using the CS algorithm are also compared with those using the HCS and PSO algorithms. The comparison demonstrates that the convergence value and speed of the CS algorithm are always better than those of the HCS and PSO algorithms in the MPPT application in the DFIG-WECS.

The existence of multiple decision-makers and goals, spatial and non-linear forest management objectives and the combinatorial nature of forest planning problems are reasons that support the use of heuristic optimisation algorithms in forest planning instead of the more traditional LP methods. A heuristic is a search algorithm that does not necessarily find the global optimum but it can produce relatively good solutions within reasonable time. The performance of different heuristics may vary depending on the complexity of the planning problem. This study tested six heuristic optimisation techniques in five different, increasingly difficult planning problems. The heuristics were evaluated with respect to the objective function value that the techniques were able to find, and the time they consumed in the optimisation process. The tested optimisation techniques were 1) random ascent (RA), 2) Hero sequential ascent technique (Hero), 3) simulated annealing (SA), 4) a hybrid of SA and Hero (SA+Hero), 5) tabu search (TS) and 6) genetic algorithm (GA). The results, calculated as averages of 100 repeated optimisations, were very similar for all heuristics with respect to the objective function value but the time consumption of the heuristics varied considerably. During the time the slowest techniques (SA or GA) required for convergence, the optimisation could have been repeated about 200 times with the fastest technique (Hero). The SA+Hero and SA techniques found the best solutions for non-spatial planning problems, while GA was the best in the most difficult problems. The results suggest that, especially in spatial planning problems, it is a benefit if the method performs more complicated moves than selecting one of the neighbouring solutions. It may also be beneficial to combine two or more heuristic techniques.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

A cuckoo search algorithm with scale-free population topology

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

Cuckoo search (CS) is one of the latest and most efficient optimization techniques developed so far. Several attempts have been made in past in order to improve the efficiency of CS algorithm. In this paper we have tried to exploit several parameters of the CS algorithm in order to increase its efficiency. Cuckoo search is a metaheuristic optimization technique. Its parameters involve the Levy distribution factor beta (β) and the probability factor (P) with which solutions are replaced with new solutions. Hence for optimum values of the aforesaid parameters, efficiency of CS algorithm can be improved and can be used to solve optimization problems.

The benefits of reactive power compensation depend greatly on the placement and size of the compensators. STATCOM is a shunt compensator and one of the important members of the Flexible AC Transmission Systems (FACTS) family that are increasingly being used in long transmission lines in modern power systems. The FACTS devices placement problem is commonly solved using heuristic optimization techniques which are diverse and have been the subject of ongoing enhancements. This chapter, in the first stage, presents a survey of the literature from the last decade that has focused on the various heuristic optimization techniques applied to determine optimal placement and sizing of the STATCOM. In the second stage, the application of the global harmony search (GHS) algorithm as a new meta-heuristic optimization method for determining the optimal location and size of STATCOM in a transmission network is presented. The algorithm is easy to be implemented and capable of finding multiple optimal solutions to the constrained multi-objective problem, providing more flexibility in making decisions about the location of STATCOM. Power system loss reduction, bus voltage profile improvement, voltage stability enhancement and device size are employed as measures of power system performance in the optimization algorithm. The proposed multi-objective GHS algorithm is validated on 57 and 118-bus transmission networks.KeywordsSTATCOMPower lossModal analysisOptimization techniquesHarmony search algorithm

Power system stability is a great concern in today's interconnected power system especially when the system is subjected to a fault. These faults occasionally lead to Low Frequency Oscillation (LFO). Therefore, Shunt Flexible AC Transmission System (FACTS) devices for example, SVC are employed to provide damping to attain system stability. The performance of SVC is totally dependent on proper tuning of its controller and usually heuristic optimization techniques are used to search the best controller parameters. In this paper, a popular metaheuristic optimization technique known as Firefly Algorithm (FA) is presented for optimal design of SVC controller in multi machine power system. In the simulation, the linearized model of power system and conventional lead-lag controller as SVC damping controller are used. The performance of obtained results using Firefly Algorithm (FA) is compared with the results obtained from Particle Swarm Optimization (PSO) Algorithm. The comparison of attained results show that FA can find more optimal parameter values of SVC damping controller and subsequently enhance power system stability.

The Cuckoo Search (CS) algorithm is an effective swarm intelligence optimization algorithm whose important developments were presented by Yang and Deb in 2009. The CS algorithm has been used in many applications to solve optimization problems. This paper describes an overview of the applications of CS in the scope of image processing to solve optimization problems for the image during the years 2015-2021. The main categories reviewed that used CS in the field of image processing are: image segmentation, image optimization, image noise removal, image classification, feature extraction in images, image clustering and edge detection. The aim of this paper is to provide an overview and summarize the literature review of applying CS algorithm in these categories in order to extract which categories that applied this algorithm more than others. From this review we conclude that CS was mostly applied in the image segmentation category to optimize the threshold search.

Design of prestressed concrete flat slab using modern heuristic optimization techniques

This review paper provides an in-depth exploration of optimization techniques in Operations Research (OR), highlighting their significance, recent advancements, challenges, and future directions. Operations Research utilizes mathematical and analytical methods to optimize decision-making processes across various industries. The paper begins with an overview of OR, discussing its definition, scope, and historical development. It then delves into the fundamentals of optimization, covering different types of optimization problems and algorithms. Classical optimization techniques such as Linear Programming, Integer Programming, and Nonlinear Programming are discussed, followed by an exploration of heuristic and metaheuristic optimization techniques including Genetic Algorithms, Simulated Annealing, Tabu Search, Particle Swarm Optimization, and Ant Colony Optimization. Recent advances in optimization, including hybrid methods, multi-objective optimization, optimization for big data, and integration with machine learning and artificial intelligence, are analyzed. Furthermore, the paper examines the challenges faced by optimization research and outlines emerging trends and future prospects. By synthesizing current knowledge and identifying areas for future research, this paper aims to contribute to the ongoing development and application of optimization techniques in OR and related fields.

In this paper, a novel hybrid Invasive Weed Optimization (IWO) and Cuckoo Search (CS) algorithm (IWO/CS) is presented for phase-only pattern synthesis of large array antenna. The hybrid IWO/CS algorithm embeds the Levy flight of CS algorithm into IWO algorithm as a global guide, thereby enhancing the global search ability of the algorithm. At the same time, in order to avoid trapping into the local optimal solution, mutation operator is introduced to enhance search ability of the algorithm. Finally, competitive exclusion operation is improved to maintain the diversity of population. The hybrid algorithm is applied to phase-only beam shaping to achieve main lobe broadening and side lobe depression. Simulation results demonstrate the superiority of the hybrid IWO and CS algorithm.