In this paper, we propose a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and on Simulated Annealing (SA) for reducing memory energy consumption in embedded systems. Our hybrid algorithm outperforms the Tabu Search (TS) approach. In fact, nearly from 76% up to 98% less energy consumption is recorded.

A new hybrid algorithm of Particle Swarm Optimization and Genetic Algorithm (PSOGA) is presented to get the optimum design of truss structures with discrete design variables. The objective function chosen in this paper is the total weight of the truss structure, which depends on upper and lower bounds in the form of stress and displacement limits. The Particle Swarm Optimization basically model...

in this paper by combining caching and replication techniques proposed a hybrid heuristic method based on the greedy algorithm to use the benefit of each other techniques. the algorithm in each interaction compares all the contents and one that made the best benefit value is selected for replication. the hybrid approach tested in a simulation environment and the results show that hybrid algorit...

The paper introduces a hybrid Tabu Search-Evolutionary Algorithm for solving the constraint satisfaction problem, called STLEA. Extensive experimental fine-tuning of parameters of the algorithm was performed to optimise the performance of the algorithm on a commonly used test-set. The performance of the STLEA was then compared to the best known evolutionary algorithm and benchmark deterministic...

This paper introduces a hybrid evolutionary hillclimbing algorithm that quickly solves (!onstraint, Satisfaction Problems (CSPs). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent, constraint network. This hybrid out,performs a well known hill-climbing algorithm, ...

This work explores different evolutionary approaches to Protein Structure Prediction (PSP), a highly constrained problem. These are the utilization of a repair procedure, and the use of evolutionary operators whose functioning is closed in feasible space. Both approaches rely on hybridizing the evolutionary algorithm (EA) with a backtracking algorithm. The so-obtained hybrid EAs are described, ...

Evolutionary algorithms are the choice of many researchers for optimizing machining parameters. Even though evolutionary algorithms are commonly used for solving constrained optimization problems, however in practice sometimes they deliver only insignificant performance. The difficulty with evolutionary algorithms is that they start with random initial population and all its populations become ...

In the current competitive conditions, all the manufacturers’ efforts are focused on increasing the customer satisfaction as well as reducing the production and delivery costs; thus, there is an increasing concentration on the structure and principles of supply chain (SC). Accordingly, the present research investigated simultaneous optimization of the total costs of a chain and customer satisfa...

This paper  presents a new multi objective job shop scheduling with sequence-dependent setup times. The objectives are to minimize the makespan and sum of the earliness and tardiness of jobs in a time window. A mixed integer programming model is developed for the given problem that belongs to NP-hard class. In this case, traditional approaches cannot reach to an optimal solution in a reasonable...