In the design of many mechanical systems, the human designer knows its topology and shape. The goal for computational design thus results in finding a parameter set that optimizes multiple design goals. This paper presents a novel optimization method for such parametric design, the process of which is based on the human design process, but the search algorithm of which is gradient-based for eff...

We propose a multi-objective evolutionary algorithm (MOEA), named the Hyper-volume Evolutionary Algorithm (HVEA). The algorithm is characterised by three components. First, individual fitness evaluation depends on the current Pareto front, specifically on the ratio of its dominated hyper-volume to the current Pareto front hyper-volume, hence giving an indication of how close the individual is t...

In this paper a new evolutionary paradigm is proposed. A technique called Adaptive Representation Evolutionary Algorithm (AREA) based on this paradigm is designed. AREA involves dynamic alphabets for encoding solutions. Proposed adaptive representation is more compact than binary representation. Mutation is the unique variation operator. Mutations are usually more aggressive when higher alphabe...

This paper focuses on a typical problem arising in serial production, where two consecutive departments must sequence their internal work, each taking into account the requirements of the other department. Even if the considered problem is inherently multi-objective, to date the only heuristic approaches dealing with this problem use single-objective formulations, and also rely specific assumpt...

Wireless Mesh Networks are an attractive technology for providing broadband connectivity to mobile clients who are just on the edge of wired networks, and also for building self organized networks in places where wired infrastructures are not available. Routing in Wireless Mesh Networks has multiobjective nonlinear optimization problem with some constraints. This problem has been addressed by c...

Being capable of finding a set of pareto–optimal solutions in a single run, which is a necessary feature for multi–criteria decision making, Evolutionary Algorithms (EAs) has attracted many researchers and practitioners to address the solution of Multiobjective Optimization Problems (MOPs). In a previous work, we developed a Pareto Differential Evolution (PDE) algorithm to handle multiobjective...

This paper proposes and evaluates an evolutionary multiobjective optimization algorithm (EMOA) that uses a new quality indicator, called the prospect indicator, for parent selection and environmental selection operators. The prospect indicator measures the potential of each individual to reproduce offspring that dominate itself and spread out in the objective space. The prospect indicator allow...

This paper explores some simple evolutionary strategies for an elitist, steady-state Pareto-based multi-objective evolutionary algorithm. The experimental framework is based on the SEAMO algorithm which differs from other approaches in its reliance on simple population replacement strategies, rather than sophisticated selection mechanisms. The paper demonstrates that excellent results can be ob...

In this research, we proposed a new metaheuristic technique for stock portfolio multi-objective optimization employing the combination of Strength Pareto Evolutionary Algorithm (SPEA), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Arbitrage Pricing Theory (APT). To generate the more precise model, ANFIS has implemented to envisage long-term movement values of the Tehran Stock Exchange (TSE)...

in this paper, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. two objective functions, real power loss and energy not supplied index (ens), were utilized. multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...