| With this paper Modal Mutation Schemes for Evolutionary Algorithms as a generalization of the Breeder Genetic Algorithm mutation scheme are introduced and analyzed for multimodal continuous parameter optimization problems. A new scaling rule for multiple mutations is formalized and compared with a new step-size scaling for Evolution Strategies. A performance comparison of the Multivalued Evol...

In this paper we introduce the idea of descriptor combination by Particle Swarm Optimization and its applications for classification purposes using a recently pattern recognition technique called Optimum-Path Forest (OPF), which interprets the samples of the dataset as the nodes of a given graph, and each arc is weighted by the distance between the corresponding nodes. The method combines diffe...

We investigate the robustness against both random and targeted node removal of networks in which P(k), the distribution of nodes with degree k, is a multimodal distribution, [formula--see text] with k(i) proportional to b -(i-1) and Dirac's delta function delta (x). We refer to this type of network as a scale-free multimodal network. For m=2, the network is a bimodal network; in the limit m app...

The paper describes an optimization method to support the conceptual stages of structural design. The method, called Blended Close Harmony Search, is a population-based metaheuristic approach that incorporates the following key features: 1) decision variable types that include real numbers defining geometry, discrete structural sections, and topology variables of enumeration (e.g. how many bays...

Gravitational search algorithm (GSA) has been recently presented as a new heuristic search algorithm with good results in real-valued and binary encoded optimization problems which is categorized in swarm intelligence optimization techniques. The aim of this article is to show that GSA is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, namely N...

In multimodal function optimization, niching techniques create diversification within the population, thus encouraging heterogeneous convergence. The key to the effective diversification is to identify the similarity among individuals. Without knowledge of the fitness landscape, it is usually determined by uninformative assumptions. In this article, we propose a method to estimate the sharing d...

Particle swarm optimization is a heuristic global optimization method put forward originally by Doctor Kennedy and Eberhart in 1995. Various efforts have been made for solving unimodal and multimodal problems as well as two dimensional to multidimensional problems. Efforts were put towards topology of communication, parameter adjustment, initial distribution of particles and efficient problem s...

In this article we provide a comprehensive review of the different evolutionary algorithm techniques used to address multimodal optimization problems, classifying them according to the nature of their approach. On the one hand there are algorithms that address the issue of the early convergence to a local optimum by differentiating the individuals of the population into groups and limiting thei...

Today, driving convenience has increased greatly owing to the availability of various telematics devices developed recently. However, this convenience often comes at the cost of driving safety. With the aim of achieving a balance between them, we propose a multi-modal interface for optimizing driving workload and describe an efficient design for the interface. To demonstrate the effectiveness o...

The swarm intelligence of animals is a natural paradigm to apply to optimization problems. Ant colony, bee colony, firefly and bat algorithms are amongst those that have been demonstrated to efficiently to optimize complex constraints. This paper proposes the new Sparkling Squid Algorithm (SSA) for multimodal optimization, inspired by the intelligent swarm behavior of its namesake. After an int...