Recently, energy efficiency has gained attention from researchers interested in optimizing computing resources. Solving real-world problems using optimization techniques (such as metaheuristics) requires a large number of computing resources and time, consuming an enormous amount of energy. However, only a few and limited research efforts in studying the energy consumption of metaheuristics can be found in the existing literature. In particular, genetic algorithms (GAs) are being used so widely to solve a large range of problems in scientific and real-world problems, but hardly found explained in their internal consumption behavior. In the present article, we analyze the energy consumption behavior of such techniques to offer a useful set of findings to researchers in the mentioned domains. We expand our study to include several algorithms and different problems and target the components of the algorithms so that the results are still more appealing for researchers in arbitrary domains of application. Our experiments on the sequential GAs show the controlling role of the fitness operator on energy consumption and also reveal possible energy hot spots in GAs operations, such as mutation operator. Further, our distributed evaluations besides a statistical analysis of the results demonstrate that the communication scheme could highly affect the energy consumption of the parallel evaluations of the GAs.

Genetic Algorithms (GAs) are being used to solve a wide range of problems in real world problems, and it is important to study their implementations to improve the solution quality and reduce the execution time. Designing parallel (e.g., distributed) GAs is one research line to do so. In distributed GAs, every individual represents a tentative solution. Individuals are split (and sparsely communicated) over many islands, with genetic operators being applied locally in each island. In addition, in order to maintain diversity and reduce the number of the evaluations, a migration operator is used to enhance their behavior. This article presents a basic study on the speed-up of parallel GAs where a common approach is followed to better understand synchronous and asynchronous versions together. We analyze the behavior of GAs over a homogeneous multiprocessor system. We will report results showing linear and even super linear speed-up in both cases of study. The parallel performance of the synchronous and asynchronous versions is very good in a multiprocessor computer, both in terms of time and solution quality. Besides, a statistical analysis of the algorithms clearly proves that both cases have a similar numerical behavior over a homogeneous parallel system.

Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GAs) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GAs (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming of sequential GAs. In the case of applying PGAs, we can expect better performance, the reason being the exchange of knowledge during the parallel search process. The resulting distributed search is different compared to what sequential panmictic GAs do, then deserving additional studies. This article presents a performance study of three different PGAs. Moreover, we investigate the effect of synchronizing communications over modern shared-memory multiprocessors. We consider the master-slave model along with synchronous and asynchronous distributed GAs

Optimization arises everywhere in industrial and engineering fields, with complex and time-consuming problems to be solved. Exact search techniques cannot afford practical solutions for most of the real-life problems in reasonable time-bound. Metaheuristics proved to be numerically efficient solvers for such problems in terms of solution quality, however, they could require large time and energy to get the optimal solution. Parallelization (i.e., distributed) is a promising approach for overcoming the overwhelming energy and time consumption values of these methods. Despite recent approaches in running metaheuristics in parallel, the community still lacks for novel studies comparing and benchmarking the canonical optimization techniques while being running in parallel. In this work, we present two extensive studies to the solution quality, energy consumption, and execution time for three different metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their distributed counterparts. The main aim of our studies is exploring the efficiency of parallel execution of the metaheuristics while being running in new computing environments. Here, we want to identify the combinatorics between metaheuristics and solving optimization problems while being run in parallel. For our studies, we consider a multicore machine with 32 cores. This choice to a recent and commonly used system shall enrich the existing literature for multicore systems against the enormous existing studies over cluster systems. The analyses and discussions for the results of the different algorithms exhibit the combinatorics between the different metaheuristics and the parallel execution using a different number of cores. The outcome of these studies builds a guide for future designs of efficient and energy-aware optimization techniques.

Abstract—Hankel transform was employed to solve the twodimensional steady state advection–diffusion equation considering
a continuous point source with vertical eddy diffusivity as a power
law of vertical height and downwind distance, also, taking wind
speed as power law. The analytical model was evaluated and
compared with Hanford diffusion experiment in stable conditions
and Copenhagen diffusion experiment in unstable and neutral
conditions which was done by reducing the general analytical
model to a one with linear vertical eddy diffusivity and constant
downwind speed profile. Comparison with other analytical models
was held. The presented model predictions show a good agreement
with observations and lay inside a factor of two with observed data
of both Hanford and Copenhagen diffusion experiments.

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Abstract: A series of new 2-pyrazoline analogues was synthesized. The structures of the synthesized
compounds were elucidated by the analytical and spectroscopic data. Some selected compounds were screened
for the anti-inflammatory activity by using the animal model of carrageenan-induced paw edema in mice.
Additionally, the analgesic and acute toxicity of these compounds were evaluated and exhibited reasonable
results. The anti-oxidant and anti-inflammatory effects of these compounds were established by measuring the
contents of malondialdehyde (MDA), reduced glutathione (GSH), nitric oxide (NO), and tumor necrosis factor
alpha (TNF-α) in the edema paw tissue. The results revealed that compounds 5, 6, 7, and 15 could be
recognized as potential multi-potent anti-inflammatory agents.

Abstract: A series of new 2-pyrazoline analogues was synthesized. The structures of the synthesized
compounds were elucidated by the analytical and spectroscopic data. Some selected compounds were screened
for the anti-inflammatory activity by using the animal model of carrageenan-induced paw edema in mice.
Additionally, the analgesic and acute toxicity of these compounds were evaluated and exhibited reasonable
results. The anti-oxidant and anti-inflammatory effects of these compounds were established by measuring the
contents of malondialdehyde (MDA), reduced glutathione (GSH), nitric oxide (NO), and tumor necrosis factor
alpha (TNF-α) in the edema paw tissue. The results revealed that compounds 5, 6, 7, and 15 could be
recognized as potential multi-potent anti-inflammatory agents.

Abstract: Nonclassical effects are investigated in a system formed by two quantum wells, each of
which is inside an open cavity. The cavities are spatially separated, linked by a fiber, and filled
with a linear optical medium. Based on Husimi distributions (HDs) andWehrl entropy, we explore
the effects of the physical parameters on the generation and the robustness of the mixedness and
HD information in the phase space. The generated quantum coherence and the HD information
depend crucially on the cavity-exciton and fiber cavity couplings as well as on the optical medium
density. The HD information and purity are lost due to the dissipation. This loss may be inhibited by
increasing the optical susceptibility as well as the couplings of the exciton-cavity and the fiber-cavity.
These parameters control the regularity, amplitudes, and frequencies of the generated mixedness.

We explore two open cavity-qubit systems linked via an optical fiber. The generation of
non-local correlations (NLCs) between the two qubits, via skew-information and Bell-function, are
investigated under the effects of the physical parameters of the initial state and the interaction couplings of the cavity-qubit and the cavity-fiber and the environment. It is found that the robustness
and the generation of the NLCs depend on the physical parameters. Under the condition that the
cavity-qubit couplings is much smaller than the cavity-fiber coupling, the regularity of the growth
and the oscillations of the NLCs are enhanced and are more robust