The paper describes software combining parametric geometric modeling with a version of the harmony search method, modified to support multimodal structural optimization. Researchers have recognized the potential of population-based optimization methods, such as genetic algorithms, to support multimodal optimization: that is, generating a diverse range of good alternative solutions, rather than ...

This paper assumes each user has more than one queue, derives a new packet-dependant proportional fairness power allocation pattern based on the sum of weight capacity and the packet’s priority in users’ queues, and proposes 4 new cross-layer packet-dependant OFDM scheduling schemes based on proportional fairness for heterogeneous classes of traffic. Scenario 1, scenario 2 and scenario 3 lead r...

In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is extended with parallelization on one hand and an adaptive operators method on the other. The parallelization of the algorithm is achieved through multithreading mechanism, a very effective and easy to implement technique. ...

This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighbourhood best values, for solving multimodal optimization problems. In the proposed speciesbased PSO (SPSO), the swarm population is divided into species subpopulations based on their similarity. Each species is grouped around a dominating particle called the species seed. At each iteratio...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO...

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly a...

Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint function...

In this paper, five previous Particle Swarm Optimization (PSO) algorithms for multimodal function optimization are reviewed. A new and a successful PSO based algorithm, named as CPSO is proposed. CPSO enhances the exploration and exploitation capabilities of PSO by performing search using a random walk and a hill climbing components. Furthermore, one of the previous PSO approaches is improved i...