6. ConclusionThe present study Y-27632 CAS concludes that the cheaply available industrial byproducts have a good potential and can be used as substrates for the production of different value-added products such as commercial enzymes from different microorganisms using either submerged or solid state fermentations.Conflict of InterestsThe authors declare that there is no conflict of interests regarding the publication of this paper.
The idea of Particle Swarm Optimization (PSO) stems from biology where a swarm of birds coordinates itself in order to achieve a goal. When a swarm of birds looks for food, its individuals will spread in the environment and move around independently with some degree of randomness which enables it to discover food accumulations.

After a while, one of them will find something digestible and, being social, communicates this to its neighbors. These can then approach the source of food, thus leading to the convergence of the swarm to the source of food. Following this analogy, PSO was largely derived from sociopsychology concept and transferred to optimization [1], where each particle (bird) uses the local information regarding the displacement of its reachable closer neighbors to decide on its own displacement, resulting to complex and adaptive collective behaviors.Since the inception of PSO technique, a lot of work has been done by researchers to enhance its efficiency in handling optimization problems. One such work is the introduction of linear decreasing inertia weight (LDIW) strategy into the original PSO to control its exploration and exploitation for better performance [2�C4].

However, LDIW-PSO algorithm from the literature is known to have the shortcoming of premature convergence in solving complex (multipeak) problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. The challenge of addressing this shortcoming has been on for a long time and has attracted much attention of researchers in the field of global optimization. Consequently upon this, many other inertia weight PSO variants have been proposed [2, 5�C16], with different levels of successes. Some of these variants have claimed better performances over LDIW-PSO, thereby making it look weak or inferior. Also, since improving on the performance of PSO is an area which still attracts more researchers, this paper strives to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters, like velocity limits for the particles, are properly set. Using the experimentally obtained values for particle velocity limits in LDIW-PSO, results show that LDIW-PSO outperformed other PSO Brefeldin_A variants adopted for comparison.