Striking a balance between objective optimization and constraint satisfaction is essential for solving constrained multi-objective problems (CMOPs). Nevertheless, most existing evolutionary algorithms face significant challenges on CMOPs with intricate infeasible regions. To tackle these challenges, this paper proposes an adaptive two-population algorithm, named ATEA, which dynamically exploits...

During the last years, cooperating coevolutionary algorithms could improve convergence of several optimization benchmarks significantly by placing each dimension of the search space in its own subpopulation. Though, their general applicability is restricted by problems with epistatic links between problem dimensions – a major obstacle in cooperating coevolutionary function optimization. This wo...

Small populations are very desirable for reducing the required computational resources in evolutionary optimization of complex real-world problems. Unfortunately, the search performance of small populations often reduces dramatically in a large search space. To addresses this problem, a method to find an optimal search dimension for small populations is suggested in this paper. The basic idea i...

A two-population Genetic Algorithm for constrained optimization is exercised and analyzed. One population consists of feasible candidate solutions evolving toward optimality. Their infeasible but promising offspring are transferred to a second, infeasible population. Four striking features are illustrated by executing challenge problems from the literature. First, both populations evolve essent...

The Economic Load Dispatch (ELD) problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA), for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to lim...

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...

For the first time, a running time analysis of a multi-objective evolutionary algorithm for a discrete optimization problem is given. To this end, a simple pseudo-Boolean problem (Lotz: leading ones trailing zeroes) is defined and a population-based optimization algorithm (FEMO). We show, that the algorithm performs a black box optimization in Θ(n log n) function evaluations where n is the numb...

Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining th...

In thermoelectric coolers (TECs) applied external voltage potential is generated to a temperature difference based on the Peltier effect. Main and basic structure of TECs is in the form of single stage device. Due to the low efficiency, especially low coefficient of performance (COP) of thermoelectric coolers, optimal design of geometrical parameters of such devices is vital. For this purpose, ...

This article introduces two swarm intelligent algorithms, a group search optimizer (GSO) and an artificial fish swarm algorithm (AFSA). A single intelligent algorithm always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these algorithms to create a new hybrid optimization algorithm known as the group search-artif...