A Novel Hybrid Optimization Algorithm Based on GA and ACO for Solving Complex Problem

Multilevel Inverters are now increasingly used in most of the power electronics applications including medium and high voltage devices. SHE is useful for eliminating the switching losses particularly for high voltage applications. This paper presents selective harmonic elimination through Hybrid optimization algorithm for elimination of lower order odd harmonics and THD in the multilevel inverter output. This hybrid algorithm is a combination of Ant colony optimization and Newton-Raphson algorithm where ant Colony optimization algorithm is applied for first four cycles and then the output of this algorithm will be treated as a starting consideration for N- _R algorithm. The primary advantage of this algorithm is its simplicity of design, less computational efforts, faster convergence and elimination of need of initial guess compared to other conventional iterative methods. Results show that third, fifth and seventh harmonics along with THD are minimized greatly. MATLAB/Simulink software is used to simulate these results. Experimental results are also presented to prove the efficacy of proposed system.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

This paper presents a methodology to solve the manufacturing cell creation and the production scheduling problems for designing virtual cellular manufacturing systems (VCMSs). The objective is to minimize the total materials and components travelling distance incurred. The methodology consists of (i) a mathematical model that describes the characteristics of a VCMS and includes constraints such as delivery due dates of products, maximum capacities of resources, critical tools limitation, and (ii) an ant colony optimization (ACO) algorithm for manufacturing cell formation and production scheduling. Since the proposed ACO algorithm does not indicate a feasible schedule explicitly, two simple heuristics are developed to assign workstations to the operations of the jobs, and to construct the final schedule. To demonstrate the effectiveness of the proposed methodology, both the ACO algorithm and the genetic algorithm are applied to design manufacturing cells for a company in China producing internal combustion engine components. Comparison of the results obtained with the results supplied by the company on the existing manufacturing system show that both the ACO algorithm and the genetic algorithm together with the virtual cellular manufacturing concept perform better than the current manufacturing practice in terms of average workstation utilization, product completion time and system throughput. Also, the results of a set of randomly generated numerical experiments show that the proposed ACO algorithm generates excellent final solutions in a much shorter computation time when compared with the genetic algorithm. Therefore, the mathematical model and the ACO algorithm proposed in this paper form a simple, but effective and efficient methodology to solve the manufacturing cell creation and production scheduling problems for designing VCMSs.

Wireless Sensor Networks (WSN) utilizes more nodes to forward messages and information from source node to destination node and these nodes are battery-powered devices. Thus, Energy saving is always vital role of Routing Protocols which need to maximize the lifetime of WSN. Mobile ad hoc networks (MANET) are self-starting and self-constructing multichip wireless networks in which, the assembly of the system deviates vigorously. This is mainly due to the mobility of the nodes. The nodes in the network not only act as hosts but also as routers that route data from other nodes in network. MANETs require an efficient routing protocol that achieves the quality of service (QoS) mechanism. They are not considering the mobility as one constraint for those problems. Because, mobility of nodes causes frequent link failures and high error rates, so it makes difficult to maintain the desired QoS in the network. To overcome those problems, we propose mobility aware routing protocol for MANET using hybrid optimization (MARP-HO) algorithm, which maximizes the QoS in data transmission. The proposed routing protocol comprises of two phases. The clustering process consists of cluster formation and cluster head (CH) selection. First, the energy efficient clustering is done by improved animal migration optimization (IAMO) algorithm. Then, we gather multiple constraints are energy consumption, received signal strength, mobility and cooperative rate from each cluster member. The gathered constraints used to compute the node cost of each cluster member, here the highest cost node act as CH of cluster. Second, we compute routing path between source–destination using improved ant colony optimization (IACO) algorithm. The proposed MARP-HO routing protocol is compared with existing state-of-art routing protocols in terms of energy consumption, delivery ratio, loss ratio, link stability, delay, network lifetime, number of dead nodes and throughput.

An enhanced ACO algorithm to select features for text categorization and its parallelization

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

Interferometric Synthetic Aperture Radar (InSAR) is a microwave imaging system with all-weather earth observation. Because of its wide observation area, high precision, day and night operational capabilities, it has a significant advantage in monitoring large-scale surface deformation. Phase unwrapping is a key step in InSAR data processing. The core of Goldstein traditional branch unwrapping method is the establishment of branch-cutting lines. The shorter the overall length of the branch line, the solution is more accurate. Based on the idea of solving the shortest path of Travelling Salesman Problem (TSP), a new algorithm combining ant colony algorithm and genetic algorithm is proposed. Based on the advantages of these two algorithms, we avoid the disadvantages of excessive parameters, slow calculation speed and low efficiency in ant colony algorithm, reduce the time consumed by ant colony algorithm in solving local optimal solution, improve the local search ability of ant colony algorithm, establish shorter branch line than traditional branch method, prevent islanding effect and improve the precision of phase unwrapping. The tests performed on real and simulated interferometric SAR data confirm the validity of the proposed method.

This paper presents a novel intelligent and effective method based on an improved ant colony optimization (ACO) algorithm to solve the multi-objective ship weather routing optimization problem, considering the navigation safety, fuel consumption, and sailing time. Here the improvement of the ACO algorithm is mainly reflected in two aspects. First, to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route, the basic parameters of the algorithm are improved, and new control factors are introduced. Second, to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems, the related operations of crossover, recombination, and mutation in the genetic algorithm are introduced in the improved ACO algorithm. The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems. In addition, the black-box model method was used to study the ship fuel consumption during a voyage; the model was constructed based on an artificial neural network. The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship, and then the trained black-box model was used to predict the future fuel consumption of the test ship. Compared with other fuel consumption calculation methods, the black-box model method showed higher accuracy and applicability.

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the optimal design and operation of water distribution systems. More recently, ant colony optimization algorithms (ACOAs), which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. In this paper, a formulation is developed which enables ACOAs to be used for the optimal design of water distribution systems. This formulation is applied to two benchmark water distribution system optimization problems and the results are compared with those obtained using genetic algorithms. The findings of this study indicate that ACOAs are an attractive alternative to GAs for the optimal design of water distribution systems, as they outperformed GAs for the two case studies considered both in terms of computational efficiency and their ability to find near global optimal solutions.

Ant colony optimization (ACO) algorithm is a recent meta-heuristic method inspired by the behavior of real ant colonies. The algorithm uses parallel computation mechanism and performs strong robustness, but it faces the limitations of stagnation and premature convergence. In this paper, a hybrid PS-ACO algorithm, ACO algorithm modified by particle swarm optimization (PSO) algorithm, is presented. The pheromone updating rules of ACO are combined with the local and global search mechanisms of PSO. On one hand, the search space is expanded by the local exploration; on the other hand, the search process is directed by the global experience. The local and global search mechanisms are combined stochastically to balance the exploration and the exploitation, so that the search efficiency can be improved. The convergence analysis and parameters selection are given through simulations on traveling salesman problems (TSP). The results show that the hybrid PS-ACO algorithm has better convergence performance than genetic algorithm (GA), ACO and MMAS under the condition of limited evolution iterations.

Non-convex optimization problems are prevalent in scientific and engineering applications, often characterized by multiple local minima that challenge standard optimization techniques. Global optimization methods, such as Genetic Algorithms (GA) (1992) [4] and Particle Swarm Optimization (PSO) (1995) [6], provide robust global exploration but tend to converge slowly. Conversely, local search methods, such as Newton’s method (2004) [1], (2006) [7], and the Conjugate Gradient (CG) (1964) [3] method, offer rapid convergence but are susceptible to local minima. In this paper, we present four hybrid optimization algorithms that integrate the exploratory strength of global algorithms with the refinement ability of local methods. The proposed hybrids, Genetic Algorithms (GA) + Newton, GA + CG, PSO + Newton, and PSO + CG, are evaluated against their standalone counterparts on standard benchmark functions (2013) [5], including Rosenbrock, Rastrigin, Ackley, and Himmelblau. This work builds upon our previous research on the hybridization of two global search algorithms, the Nelder–Mead (simplex) algorithm and the Bat algorithm, which resulted in the Hybrid Simplex Bat Algorithm (HSBA) [2], recently Published. Results demonstrate that the hybrid approaches consistently outperform traditional methods in terms of accuracy, convergence rate, and computational efficiency.

Over the years, Metaheuristic algorithms have proven significant efficacy in handling None- Deterministic polynomial hard (NP-hard) combinatorial optimization problems, mainly the Traveling Salesman Problem (TSP) which has been used to evaluate and test many metaheuristics algorithms such as Ant Colony optimizations. This paper contributes a new optimization algorithm to the famous-family of swarm and evolutionary techniques named Scuba Diver Optimization Algorithm (SDOA) which is a novel population-based metaheuristic optimization algorithm inspired by the behavioural and physiological dynamics of scuba divers in the open water environments. SDOA simulates main diving factors including (depth, tank pressure/oxygen, bottom time, and ascent strategies) for keeping a balance between explorations and exploitations during the search process. SDOA novelty appears in its adaptive control of solution diversity using pressure and oxygen level thresholds, enabling robust avoidance of local optima and faster convergence. The SDOA algorithm was evaluated on a range of TSP instances from the TSPLIB library, which were categorized into three groups: Group 1, comprising instances with 1–99 cities; Group 2, including instances with 100–999 cities; and Group 3, consisting of large-scale instances with 1000 or more cities. And the results of SDOA is compared with established metaheuristics algorithms such as Ant Colony Optimization, Artificial Bee Colony, and Genetic Algorithm. The study results showing that SDOA consistently produces shorter (near optimal) tour lengths with significantly minimum computational time in larger problem sizes (Group 3). The proposed algorithm also demonstrates better scalability and stability in different initialization and population sizes. Future directions include adapting SDOA for solving other optimization problems including multi-objective or dynamic optimization contexts.

Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism