This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.

Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

http://www.soft-computing.de/SL_PSO_published.pdf

A social learning particle swarm optimization algorithm for scalable optimization

This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms.

Emperor penguin optimizer: A bio-inspired algorithm for engineering problems

A survey on new generation metaheuristic algorithms

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions.

Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

AFSA (artificial fish-swarm algorithm) is one of the best methods of optimization among the swarm intelligence algorithms. This algorithm is inspired by the collective movement of the fish and their various social behaviors. Based on a series of instinctive behaviors, the fish always try to maintain their colonies and accordingly demonstrate intelligent behaviors. Searching for food, immigration and dealing with dangers all happen in a social form and interactions between all fish in a group will result in an intelligent social behavior.This algorithm has many advantages including high convergence speed, flexibility, fault tolerance and high accuracy. This paper is a review of AFSA algorithm and describes the evolution of this algorithm along with all improvements, its combination with various methods as well as its applications. There are many optimization methods which have a affinity with this method and the result of this combination will improve the performance of this method. Its disadvantages include high time complexity, lack of balance between global and local search, in addition to lack of benefiting from the experiences of group members for the next movements.

Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications

This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.

Swallow swarm optimization algorithm: a new method to optimization

The Swarm Intelligence is a new and modern method employed in optimization problems. The Swarm Intelligence method is based on the en masse movement of living animals like birds, fishes, ants and other social animals. Migration, seeking for food and fighting with enemies are INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS, VOL. 5, NO. 1, MARCH 2012

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A review of artificial fish swarm optimization methods and applications

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (Dmax), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of Dmax and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of Dmax and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system’s results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.

Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems

Increasing customers’ satisfaction in this developed world is the most important factor to have a successful trade and production. New marketing methods and supervising the marketing choices will have a key role to increase the profit of a company. This paper investigates an expert system through four main principles of marketing (price, product, Place and Promotion) and their composition with a logic fuzzy system and benefiting from the experiences of marketing specialists. Comparing with the other systems, this one has special properties such as investigating and extracting different fields in which affect the customers’ satisfaction directly or indirectly as input parameters (26), using knowledge of experts to design inference system rule, composing the results of five fuzzy expert systems and calculating final result(customer’s satisfaction) and finally creating a high function expert system on management and guiding the managers to do a successful marketing in dynamic markets.

/pdf/fhesmm-fuzzy-hybrid-expert-system-for-marketing-mix-model-1kkjtig5i8.pdf

Ghodrat Sepidnam

Papers

Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications

Swallow swarm optimization algorithm: a new method to optimization

A review of artificial fish swarm optimization methods and applications

Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems

FHESMM: Fuzzy Hybrid Expert System for Marketing Mix Model